{"version":"https://jsonfeed.org/version/1","title":"Pipeline Conversations","home_page_url":"https://podcast.zenml.io","feed_url":"https://podcast.zenml.io/json","description":"Pipeline Conversations is a fortnightly podcast bringing you interviews and discussion with industry leaders, top technology professionals and others. We discuss the latest developments in machine learning, deep learning, artificial intelligence, with a particular focus on MLOps, or how trained models are used in production.","_fireside":{"subtitle":"A Machine Learning Podcast by ZenML","pubdate":"2025-01-15T08:00:00.000+01:00","explicit":false,"copyright":"2025 by ZenML GmbH","owner":"ZenML GmbH","image":"https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/4/4d525632-f8ef-47c1-9321-20f5c498b1ac/cover.jpg?v=2"},"items":[{"id":"2ac46862-3236-4293-a5e0-401e1eb47334","title":"Production LLM Security: Real-world Strategies from Industry Leaders 🔐","url":"https://podcast.zenml.io/llmops-db-security","content_text":"Learn how leading companies like Dropbox, NVIDIA, and Slack tackle LLM security in production. This comprehensive guide covers practical strategies for preventing prompt injection, securing RAG systems, and implementing multi-layered defenses, based on real-world case studies from the LLMOps database. Discover battle-tested approaches to input validation, data privacy, and monitoring for building secure AI applications.\n\nPlease read the full blog post here and the associated LLMOps database entries here.","content_html":"\u003cp\u003eLearn how leading companies like Dropbox, NVIDIA, and Slack tackle LLM security in production. This comprehensive guide covers practical strategies for preventing prompt injection, securing RAG systems, and implementing multi-layered defenses, based on real-world case studies from the LLMOps database. Discover battle-tested approaches to input validation, data privacy, and monitoring for building secure AI applications.\u003c/p\u003e\n\n\u003cp\u003ePlease read the full blog post \u003ca href=\"https://www.zenml.io/blog/production-llm-security-real-world-strategies-from-industry-leaders\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and the associated LLMOps database entries \u003ca href=\"https://zenml.io/llmops-database\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e","summary":"Learn how leading companies like Dropbox, NVIDIA, and Slack tackle LLM security in production.","date_published":"2025-01-15T08:00:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/2ac46862-3236-4293-a5e0-401e1eb47334.mp3","mime_type":"audio/mpeg","size_in_bytes":31275788,"duration_in_seconds":3095}]},{"id":"850c441c-eb0b-4d22-ad8d-3da4224b35b6","title":"Optimizing LLM Performance and Cost for LLMs in Production","url":"https://podcast.zenml.io/llmops-db-performance-and-cost-optimization","content_text":"In this episode, we dive deep into the world of LLM optimization and cost management - a critical challenge facing AI teams today. Join us as we explore real-world strategies from companies like Dropbox, Meta, and Replit who are pushing the boundaries of what's possible with large language models. From clever model selection techniques and knowledge distillation to advanced inference optimization and cost-saving strategies, we'll unpack the tools and approaches that are helping organizations squeeze maximum value from their LLM deployments. Whether you're dealing with runaway API costs, struggling with inference latency, or looking to optimize your model infrastructure, this episode provides practical insights that you can apply to your own AI initiatives. Perfect for ML engineers, technical leads, and anyone responsible for maintaining LLM systems in production.\n\nPlease read the full blog post here and the associated LLMOps database entries here.","content_html":"\u003cp\u003eIn this episode, we dive deep into the world of LLM optimization and cost management - a critical challenge facing AI teams today. Join us as we explore real-world strategies from companies like Dropbox, Meta, and Replit who are pushing the boundaries of what\u0026#39;s possible with large language models. From clever model selection techniques and knowledge distillation to advanced inference optimization and cost-saving strategies, we\u0026#39;ll unpack the tools and approaches that are helping organizations squeeze maximum value from their LLM deployments. Whether you\u0026#39;re dealing with runaway API costs, struggling with inference latency, or looking to optimize your model infrastructure, this episode provides practical insights that you can apply to your own AI initiatives. Perfect for ML engineers, technical leads, and anyone responsible for maintaining LLM systems in production.\u003c/p\u003e\n\n\u003cp\u003ePlease read the full blog post \u003ca href=\"https://www.zenml.io/blog/optimizing-llm-performance-and-cost-squeezing-every-drop-of-value\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and the associated LLMOps database entries \u003ca href=\"https://zenml.io/llmops-database\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e","summary":"A deep dive into the world of LLM optimization and cost management - a critical challenge facing AI teams today.","date_published":"2025-01-13T08:00:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/850c441c-eb0b-4d22-ad8d-3da4224b35b6.mp3","mime_type":"audio/mpeg","size_in_bytes":20921804,"duration_in_seconds":2029}]},{"id":"8254e8a5-306a-46c6-9695-ecd0daea4150","title":"The Evaluation Playbook: Making LLMs Production-Ready 🧪📈","url":"https://podcast.zenml.io/llmops-db-evaluation","content_text":"A comprehensive exploration of real-world lessons in LLM evaluation and quality assurance, examining how industry leaders tackle the challenges of assessing language models in production. \n\nThrough diverse case studies, we cover the transition from traditional ML evaluation, establishing clear metrics, combining automated and human evaluation strategies, and implementing continuous improvement cycles to ensure reliable LLM applications at scale.\n\nPlease read the full blog post here and the associated LLMOps database entries here.","content_html":"\u003cp\u003eA comprehensive exploration of real-world lessons in LLM evaluation and quality assurance, examining how industry leaders tackle the challenges of assessing language models in production. \u003c/p\u003e\n\n\u003cp\u003eThrough diverse case studies, we cover the transition from traditional ML evaluation, establishing clear metrics, combining automated and human evaluation strategies, and implementing continuous improvement cycles to ensure reliable LLM applications at scale.\u003c/p\u003e\n\n\u003cp\u003ePlease read the full blog post \u003ca href=\"https://www.zenml.io/blog/the-evaluation-playbook-making-llms-production-ready\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and the associated LLMOps database entries \u003ca href=\"https://zenml.io/llmops-database\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e","summary":"A comprehensive exploration of real-world lessons in LLM evaluation and quality assurance, examining how industry leaders tackle the challenges of assessing language models in production.","date_published":"2024-12-15T21:00:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/8254e8a5-306a-46c6-9695-ecd0daea4150.mp3","mime_type":"audio/mpeg","size_in_bytes":19728308,"duration_in_seconds":1963}]},{"id":"a1117f0f-33e9-464b-a6e5-7c7eee9d39a0","title":"Prompt Engineering \u0026 Management in Production: Practical Lessons from the LLMOps Database","url":"https://podcast.zenml.io/llmops-db-prompt-engineering","content_text":"Prompt engineering is the art and science of crafting instructions that unlock the potential of large language models (LLMs). It's a critical skill for anyone working with LLMs, whether you're building cutting-edge applications or conducting fundamental research. But what does effective prompt engineering look like in practice, and how can we systematically improve our prompts over time?\n\nTo answer these questions, we've distilled key insights and techniques from a collection of LLMOps case studies spanning diverse industries and applications. From designing robust prompts to iterative refinement, optimization strategies to management infrastructure, these battle-tested lessons provide a roadmap for prompt engineering mastery.\n\nPlease read the full blog post here and the associated LLMOps database entries here.","content_html":"\u003cp\u003ePrompt engineering is the art and science of crafting instructions that unlock the potential of large language models (LLMs). It\u0026#39;s a critical skill for anyone working with LLMs, whether you\u0026#39;re building cutting-edge applications or conducting fundamental research. But what does effective prompt engineering look like in practice, and how can we systematically improve our prompts over time?\u003c/p\u003e\n\n\u003cp\u003eTo answer these questions, we\u0026#39;ve distilled key insights and techniques from a collection of LLMOps case studies spanning diverse industries and applications. From designing robust prompts to iterative refinement, optimization strategies to management infrastructure, these battle-tested lessons provide a roadmap for prompt engineering mastery.\u003c/p\u003e\n\n\u003cp\u003ePlease read the full blog post \u003ca href=\"https://www.zenml.io/blog/prompt-engineering-management-in-production-practical-lessons-from-the-llmops-database\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and the associated LLMOps database entries \u003ca href=\"https://zenml.io/llmops-database\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e","summary":"Prompt engineering is the art and science of crafting instructions that unlock the potential of large language models (LLMs). It's a critical skill for anyone working with LLMs, whether you're building cutting-edge applications or conducting fundamental research. But what does effective prompt engineering look like in practice, and how can we systematically improve our prompts over time?","date_published":"2024-12-11T06:30:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/a1117f0f-33e9-464b-a6e5-7c7eee9d39a0.mp3","mime_type":"audio/mpeg","size_in_bytes":17832644,"duration_in_seconds":1774}]},{"id":"243fce06-742f-4c5f-83a9-c131ef0b3c16","title":"LLM Agents in Production: Architectures, Challenges, and Best Practices","url":"https://podcast.zenml.io/llmops-db-agents","content_text":"An in-depth exploration of LLM agents in production environments, covering key architectures, practical challenges, and best practices. Drawing from real-world case studies, this article examines the current state of AI agent deployment, infrastructure requirements, and critical considerations for organizations looking to implement these systems safely and effectively.\n\nPlease read the full blog post here and the associated LLMOps database entries here.","content_html":"\u003cp\u003eAn in-depth exploration of LLM agents in production environments, covering key architectures, practical challenges, and best practices. Drawing from real-world case studies, this article examines the current state of AI agent deployment, infrastructure requirements, and critical considerations for organizations looking to implement these systems safely and effectively.\u003c/p\u003e\n\n\u003cp\u003ePlease read the full blog post \u003ca href=\"https://www.zenml.io/blog/llm-agents-in-production-architectures-challenges-and-best-practices\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and the associated LLMOps database entries \u003ca href=\"https://zenml.io/llmops-database\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e","summary":"An in-depth exploration of LLM agents in production environments, covering key architectures, practical challenges, and best practices.","date_published":"2024-12-09T08:00:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/243fce06-742f-4c5f-83a9-c131ef0b3c16.mp3","mime_type":"audio/mpeg","size_in_bytes":19600940,"duration_in_seconds":1957}]},{"id":"85b0a210-9235-4176-bff0-cc81fe727c40","title":"Building Advanced Search, Retrieval, and Recommendation Systems with LLMs","url":"https://podcast.zenml.io/llmops-db-embeddings","content_text":"Discover how embeddings power modern search and recommendation systems with LLMs, using case studies from the LLMOps Database. From RAG systems to personalized recommendations, learn key strategies and best practices for building intelligent applications that truly understand user intent and deliver relevant results.\n\nPlease read the full blog post here and the associated LLMOps database entries here.","content_html":"\u003cp\u003eDiscover how embeddings power modern search and recommendation systems with LLMs, using case studies from the LLMOps Database. From RAG systems to personalized recommendations, learn key strategies and best practices for building intelligent applications that truly understand user intent and deliver relevant results.\u003c/p\u003e\n\n\u003cp\u003ePlease read the full blog post \u003ca href=\"https://www.zenml.io/blog/building-advanced-search-retrieval-and-recommendation-systems-with-llms\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and the associated LLMOps database entries \u003ca href=\"https://zenml.io/llmops-database\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e","summary":"Discover how embeddings power modern search and recommendation systems with LLMs.","date_published":"2024-12-06T08:00:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/85b0a210-9235-4176-bff0-cc81fe727c40.mp3","mime_type":"audio/mpeg","size_in_bytes":7958540,"duration_in_seconds":788}]},{"id":"a02b1d19-dac5-4d2e-bf72-f9ed97ad5cda","title":"Building LLM Applications that Know What They're Talking About 🔓🧠","url":"https://podcast.zenml.io/llmops-db-rag","content_text":"Explore real-world applications of Retrieval Augmented Generation (RAG) through case studies from leading companies. Learn how RAG enhances LLM applications with external knowledge sources, examining implementation strategies, challenges, and best practices for building more accurate and informed AI systems.\n\nPlease read the full blog post [here](www.zenml.io/blog/building-llm-applications-that-know-what-theyre-talking-about) and the associated LLMOps database entries here.","content_html":"\u003cp\u003eExplore real-world applications of Retrieval Augmented Generation (RAG) through case studies from leading companies. Learn how RAG enhances LLM applications with external knowledge sources, examining implementation strategies, challenges, and best practices for building more accurate and informed AI systems.\u003c/p\u003e\n\n\u003cp\u003ePlease read the full blog post [here](\u003ca href=\"http://www.zenml.io/blog/building-llm-applications-that-know-what-theyre-talking-about\" rel=\"nofollow\"\u003ewww.zenml.io/blog/building-llm-applications-that-know-what-theyre-talking-about\u003c/a\u003e) and the associated LLMOps database entries \u003ca href=\"https://zenml.io/llmops-database\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e","summary":"A conversation about the RAG entries in the ZenML LLMOps database","date_published":"2024-12-03T08:00:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/a02b1d19-dac5-4d2e-bf72-f9ed97ad5cda.mp3","mime_type":"audio/mpeg","size_in_bytes":12903548,"duration_in_seconds":1283}]},{"id":"0e9f1c42-f2a3-471e-81c5-7bf31b3490f2","title":"Demystifying LLMOps: A Practical Database of Real-World Generative AI Implementations","url":"https://podcast.zenml.io/demystifying-llmops","content_text":"The LLMOps Database offers a curated collection of 300+ real-world generative AI implementations, providing technical teams with practical insights into successful LLM deployments. This searchable resource includes detailed case studies, architectural decisions, and AI-generated summaries of technical presentations to help bridge the gap between demos and production systems.\n\nPlease read the full blog post here and the associated database entries here.","content_html":"\u003cp\u003eThe LLMOps Database offers a curated collection of 300+ real-world generative AI implementations, providing technical teams with practical insights into successful LLM deployments. This searchable resource includes detailed case studies, architectural decisions, and AI-generated summaries of technical presentations to help bridge the gap between demos and production systems.\u003c/p\u003e\n\n\u003cp\u003ePlease read the full blog post \u003ca href=\"https://www.zenml.io/blog/demystifying-llmops-a-practical-database-of-real-world-generative-ai-implementations\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and the associated database entries \u003ca href=\"https://zenml.io/llmops-database\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e","summary":"NotebookLM summary podcast episode of a ZenML blog around the LLMOps Database.","date_published":"2024-12-02T10:00:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/0e9f1c42-f2a3-471e-81c5-7bf31b3490f2.mp3","mime_type":"audio/mpeg","size_in_bytes":9138572,"duration_in_seconds":902}]},{"id":"f0ecb9ec-1eeb-4ef3-829d-0afe43ee70e9","title":"ML at the British Library with Daniel van Strien","url":"https://podcast.zenml.io/daniel-van-strien-british-library","content_text":"This week I spoke with Daniel van Strien, a digital curator working at the British Library. Daniel has worked on a number of projects at the intersection of archives, libraries and machine learning and I was really happy to have the chance to get to unpack some of the ways he's finding to apply these techniques and tools.\n\nIn particular, I found it interesting how important the annotation process is as part of many overall workflows, as well as how simple out-of-the-box techniques like image classification using a fine-tuned model could satisfy many low-hanging fruit-type use cases.Special Guest: Daniel van Strien.","content_html":"\u003cp\u003eThis week I spoke with Daniel van Strien, a digital curator working at the British Library. Daniel has worked on a number of projects at the intersection of archives, libraries and machine learning and I was really happy to have the chance to get to unpack some of the ways he\u0026#39;s finding to apply these techniques and tools.\u003c/p\u003e\n\n\u003cp\u003eIn particular, I found it interesting how important the annotation process is as part of many overall workflows, as well as how simple out-of-the-box techniques like image classification using a fine-tuned model could satisfy many low-hanging fruit-type use cases.\u003c/p\u003e\u003cp\u003eSpecial Guest: Daniel van Strien.\u003c/p\u003e","summary":"This week I spoke with Daniel van Strien, a digital curator working at the British Library. Daniel has worked on a number of projects at the intersection of archives, libraries and machine learning and I was really happy to have the chance to get to unpack some of the ways he's finding to apply these techniques and tools.","date_published":"2022-11-10T17:00:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/f0ecb9ec-1eeb-4ef3-829d-0afe43ee70e9.mp3","mime_type":"audio/mpeg","size_in_bytes":41376868,"duration_in_seconds":3448}]},{"id":"253cd080-cfca-4b29-9a53-1641ec9b384b","title":"Questioning MLOps with Lak Lakshmanan","url":"https://podcast.zenml.io/lak-lakshmanan","content_text":"This week I spoke with Lak Lakhshmanan, who worked for years at Google on ML and AI projects and products at a senior level and he also brings years of experience working on meteorology and other scientific projects previously.\n\nLak brings a ton of experience to the table and it was interesting to hear his suggestions around when it is and isn't appropriate to bring the full set of MLOps tools to the table, for example. We also discussed the fundamentals of doing ML-backed projects as well as the teams needed to make those projects succeed.Special Guest: Lak Lakshmanan.Links:Lak on LinkedInlak lakshmanan (@lak_luster) / TwitterValliappa Lakshmanan (Lak) - HomeLak Lakshmanan – MediumAmazon.com: Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps: 9781098115784: Lakshmanan, Valliappa, Robinson, Sara, Munn, Michael: BooksAmazon.com: Practical Machine Learning for Computer Vision eBook : Lakshmanan, Valliappa, Görner, Martin, Gillard, Ryan: Kindle StoreAmazon.com: Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale eBook : Lakshmanan, Valliappa, Tigani, Jordan: Kindle StoreAmazon.com: Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning: 9781098118952: Lakshmanan, Valliappa: Books","content_html":"\u003cp\u003eThis week I spoke with Lak Lakhshmanan, who worked for years at Google on ML and AI projects and products at a senior level and he also brings years of experience working on meteorology and other scientific projects previously.\u003c/p\u003e\n\n\u003cp\u003eLak brings a ton of experience to the table and it was interesting to hear his suggestions around when it is and isn\u0026#39;t appropriate to bring the full set of MLOps tools to the table, for example. We also discussed the fundamentals of doing ML-backed projects as well as the teams needed to make those projects succeed.\u003c/p\u003e\u003cp\u003eSpecial Guest: Lak Lakshmanan.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Lak on LinkedIn\" rel=\"nofollow\" href=\"https://www.linkedin.com/in/valliappalakshmanan/\"\u003eLak on LinkedIn\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"lak lakshmanan (@lak_luster) / Twitter\" rel=\"nofollow\" href=\"https://twitter.com/lak_luster\"\u003elak lakshmanan (@lak_luster) / Twitter\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Valliappa Lakshmanan (Lak) - Home\" rel=\"nofollow\" href=\"https://aisoftwarellc.weebly.com/\"\u003eValliappa Lakshmanan (Lak) - Home\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Lak Lakshmanan – Medium\" rel=\"nofollow\" href=\"https://lakshmanok.medium.com/\"\u003eLak Lakshmanan – Medium\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Amazon.com: Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps: 9781098115784: Lakshmanan, Valliappa, Robinson, Sara, Munn, Michael: Books\" rel=\"nofollow\" href=\"https://www.amazon.com/Machine-Learning-Design-Patterns-Preparation/dp/1098115783\"\u003eAmazon.com: Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps: 9781098115784: Lakshmanan, Valliappa, Robinson, Sara, Munn, Michael: Books\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Amazon.com: Practical Machine Learning for Computer Vision eBook : Lakshmanan, Valliappa, Görner, Martin, Gillard, Ryan: Kindle Store\" rel=\"nofollow\" href=\"https://www.amazon.com/gp/product/B09B164FBM/\"\u003eAmazon.com: Practical Machine Learning for Computer Vision eBook : Lakshmanan, Valliappa, Görner, Martin, Gillard, Ryan: Kindle Store\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Amazon.com: Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale eBook : Lakshmanan, Valliappa, Tigani, Jordan: Kindle Store\" rel=\"nofollow\" href=\"https://www.amazon.com/gp/product/B07ZHQ3MGN/\"\u003eAmazon.com: Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale eBook : Lakshmanan, Valliappa, Tigani, Jordan: Kindle Store\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Amazon.com: Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning: 9781098118952: Lakshmanan, Valliappa: Books\" rel=\"nofollow\" href=\"https://www.amazon.com/Data-Science-Google-Cloud-Platform/dp/1098118952/\"\u003eAmazon.com: Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning: 9781098118952: Lakshmanan, Valliappa: Books\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"This week I spoke with Lak Lakhshmanan, who worked for years at Google on ML and AI projects and products at a senior level and he also brings years of experience working on meteorology and other scientific projects previously.","date_published":"2022-10-27T07:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/253cd080-cfca-4b29-9a53-1641ec9b384b.mp3","mime_type":"audio/mpeg","size_in_bytes":38195438,"duration_in_seconds":3182}]},{"id":"4e698579-cdee-4f8f-ba34-4bca304129c2","title":"The Full Stack with Charles Frye","url":"https://podcast.zenml.io/full-stack-charles-frye","content_text":"This week I spoke with Charles Frye. Not only has Charles volunteered to be a judge on our Month of MLOps competition happening right now, he's part of the core team working on the Full Stack Deep Learning course.\n\nNaturally, we get into education for practitioners as well as the things that Charles has seen in his own prior background working on production use cases. We also discuss the ways that tooling to support education as well as productive machine learning can and is being improved.Special Guest: Charles Frye.Links:Full Stack Deep LearningCharles 🎉 Frye (@charles_irl) / TwitterTangent Space (Charles' homepage)charlesfrye (Charles Frye)Charles Frye (LinkedIn)","content_html":"\u003cp\u003eThis week I spoke with Charles Frye. Not only has Charles volunteered to be a judge on our Month of MLOps competition happening right now, he\u0026#39;s part of the core team working on the Full Stack Deep Learning course.\u003c/p\u003e\n\n\u003cp\u003eNaturally, we get into education for practitioners as well as the things that Charles has seen in his own prior background working on production use cases. We also discuss the ways that tooling to support education as well as productive machine learning can and is being improved.\u003c/p\u003e\u003cp\u003eSpecial Guest: Charles Frye.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Full Stack Deep Learning\" rel=\"nofollow\" href=\"https://fullstackdeeplearning.com/\"\u003eFull Stack Deep Learning\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Charles 🎉 Frye (@charles_irl) / Twitter\" rel=\"nofollow\" href=\"https://twitter.com/charles_irl\"\u003eCharles 🎉 Frye (@charles_irl) / Twitter\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Tangent Space (Charles\u0026#39; homepage)\" rel=\"nofollow\" href=\"https://charlesfrye.github.io/\"\u003eTangent Space (Charles\u0026#39; homepage)\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"charlesfrye (Charles Frye)\" rel=\"nofollow\" href=\"https://github.com/charlesfrye\"\u003echarlesfrye (Charles Frye)\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Charles Frye (LinkedIn)\" rel=\"nofollow\" href=\"https://www.linkedin.com/in/charles-frye-38654abb/\"\u003eCharles Frye (LinkedIn)\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"This week I spoke with Charles Frye. Not only has Charles volunteered to be a judge on our Month of MLOps competition happening right now, he's part of the core team working on the Full Stack Deep Learning course.","date_published":"2022-10-12T07:30:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/4e698579-cdee-4f8f-ba34-4bca304129c2.mp3","mime_type":"audio/mpeg","size_in_bytes":41110130,"duration_in_seconds":3425}]},{"id":"11960a13-d427-400c-8105-382ac76bc27f","title":"Educating the next generation with Goku Mohandas","url":"https://podcast.zenml.io/goku-mohandas-made-with-ml","content_text":"In today's conversation, I'm speaking with Goku Mohandas, founder and creator of the amazing online resource MadeWithML. Goku has a bunch of practical experience, from working with Apple to a startup in the oncology space and much more.\n\nIn this conversation we continued to unpack the theme of education in ML, the challenges when it comes to working across the full stack of ML applications, and what he's seen work in his experience working on MadeWithML.\n\nWe also discuss some of the patterns he's seen in the production stacks he's seen in his experience consulting with various ML teams as well as where he sees room for improvement in the abstractions that we all rely on to do our work.\n\nGoku has generously agreed to be an external judge for our Month of MLOps competition that starts on October 10. If you haven't signed up yet, or want to learn more, please visit zenml.io/competition.Special Guest: Goku Mohandas.Links:Goku on LinkedInGokuMohandas (Goku Mohandas) on GitHubHome - Made With MLGoku Mohandas (@GokuMohandas) / TwitterMade With ML (@MadeWithML) / Twitter","content_html":"\u003cp\u003eIn today\u0026#39;s conversation, I\u0026#39;m speaking with Goku Mohandas, founder and creator of the amazing online resource \u003ca href=\"https://madewithml.com/\" rel=\"nofollow\"\u003eMadeWithML\u003c/a\u003e. Goku has a bunch of practical experience, from working with Apple to a startup in the oncology space and much more.\u003c/p\u003e\n\n\u003cp\u003eIn this conversation we continued to unpack the theme of education in ML, the challenges when it comes to working across the full stack of ML applications, and what he\u0026#39;s seen work in his experience working on \u003ca href=\"https://madewithml.com/\" rel=\"nofollow\"\u003eMadeWithML\u003c/a\u003e.\u003c/p\u003e\n\n\u003cp\u003eWe also discuss some of the patterns he\u0026#39;s seen in the production stacks he\u0026#39;s seen in his experience consulting with various ML teams as well as where he sees room for improvement in the abstractions that we all rely on to do our work.\u003c/p\u003e\n\n\u003cp\u003eGoku has generously agreed to be an external judge for our Month of MLOps competition that starts on October 10. If you haven\u0026#39;t signed up yet, or want to learn more, please visit \u003ca href=\"https://zenml.io/competition\" rel=\"nofollow\"\u003ezenml.io/competition\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eSpecial Guest: Goku Mohandas.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Goku on LinkedIn\" rel=\"nofollow\" href=\"https://www.linkedin.com/in/goku/\"\u003eGoku on LinkedIn\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"GokuMohandas (Goku Mohandas) on GitHub\" rel=\"nofollow\" href=\"https://github.com/GokuMohandas\"\u003eGokuMohandas (Goku Mohandas) on GitHub\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Home - Made With ML\" rel=\"nofollow\" href=\"https://madewithml.com/\"\u003eHome - Made With ML\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Goku Mohandas (@GokuMohandas) / Twitter\" rel=\"nofollow\" href=\"https://twitter.com/GokuMohandas\"\u003eGoku Mohandas (@GokuMohandas) / Twitter\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Made With ML (@MadeWithML) / Twitter\" rel=\"nofollow\" href=\"https://twitter.com/MadeWithML\"\u003eMade With ML (@MadeWithML) / Twitter\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"In today's conversation, I'm speaking with Goku Mohandas, founder and creator of the amazing online resource MadeWithML. Goku has a bunch of practical experience, from working with Apple to a startup in the oncology space and much more.","date_published":"2022-09-29T09:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/11960a13-d427-400c-8105-382ac76bc27f.mp3","mime_type":"audio/mpeg","size_in_bytes":49481831,"duration_in_seconds":4123}]},{"id":"20b2e352-4565-487d-ad6e-e0f865c75da5","title":"ZenML MLOps Competition","url":"https://podcast.zenml.io/mlops-competition","content_text":"So excited to be able to announce our 🔥 AMAZING 🔥 external judges for the ZenML Month of MLOps competition! We have a stellar panel of ✨ ML and MLOps heroes ✨ to help select the best pipelines from all of your submissions! \n\n💥 Charles Frye, core instructor at the amazing Full Stack Deep Learning course\n💥 Anthony Goldbloom, co-founder and former CEO of Kaggle\n💥 Chip Huyen, author of 'Designing Machine Learning Systems' and co-founder of Claypot AI\n💥 Goku Mohandas, founder of MadeWithML, another essential course in production ML\n\nWe're honoured to have them on board for the ride, and we can't wait to see all the amazing ML use cases and problems our competitors solve along the way!\n\nTo learn more about the competition and to sign up, visit https://zenml.io/competitionLinks:Sign Up for the Competition","content_html":"\u003cp\u003eSo excited to be able to announce our 🔥 AMAZING 🔥 external judges for the ZenML Month of MLOps competition! We have a stellar panel of ✨ ML and MLOps heroes ✨ to help select the best pipelines from all of your submissions! \u003c/p\u003e\n\n\u003cp\u003e💥 Charles Frye, core instructor at the amazing Full Stack Deep Learning course\u003cbr\u003e\n💥 Anthony Goldbloom, co-founder and former CEO of Kaggle\u003cbr\u003e\n💥 Chip Huyen, author of \u0026#39;Designing Machine Learning Systems\u0026#39; and co-founder of Claypot AI\u003cbr\u003e\n💥 Goku Mohandas, founder of MadeWithML, another essential course in production ML\u003c/p\u003e\n\n\u003cp\u003eWe\u0026#39;re honoured to have them on board for the ride, and we can\u0026#39;t wait to see all the amazing ML use cases and problems our competitors solve along the way!\u003c/p\u003e\n\n\u003cp\u003eTo learn more about the competition and to sign up, visit \u003ca href=\"https://zenml.io/competition\" rel=\"nofollow\"\u003ehttps://zenml.io/competition\u003c/a\u003e\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Sign Up for the Competition\" rel=\"nofollow\" href=\"https://zenml.io/competition\"\u003eSign Up for the Competition\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"So excited to be able to announce our :fire: AMAZING :fire: external judges for the ZenML Month of MLOps competition! We have a stellar panel of :sparkles: ML and MLOps heroes :sparkles: to help select the best pipelines from all of your submissions! ","date_published":"2022-09-26T14:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/20b2e352-4565-487d-ad6e-e0f865c75da5.mp3","mime_type":"audio/mpeg","size_in_bytes":5925023,"duration_in_seconds":493}]},{"id":"f7d61b52-02c8-4401-894b-92110dde2267","title":"Data-centric Computer Vision with Eric Landau","url":"https://podcast.zenml.io/data-centric-computer-vision-eric-landau-encord","content_text":"This week I spoke with Eric Landau, co-founder of Encord, a platform for data-centric computer vision. This podcast contains a lot of geekery about annotation, and even though Encord aren't an annotation tool per se, Eric and his team have tackled a bunch of quite complicated problems relating to that domain.\n\nWe also discuss the much-used term 'data-centric AI' and consider where it's useful and where perhaps there's a little bit of hype. We also get into some of the technical tradeoffs and decisions that come when building a platform. I'm really excited to get to present this episode to you today as I really enjoyed the discussion.Special Guest: Eric Landau.Links:Eric Landau (LinkedIn)Encord | The platform for data-centric computer visionEncord blogEncord (Github)Encord (@encord_team) / Twitter","content_html":"\u003cp\u003eThis week I spoke with Eric Landau, co-founder of Encord, a platform for data-centric computer vision. This podcast contains a lot of geekery about annotation, and even though Encord aren\u0026#39;t an annotation tool per se, Eric and his team have tackled a bunch of quite complicated problems relating to that domain.\u003c/p\u003e\n\n\u003cp\u003eWe also discuss the much-used term \u0026#39;data-centric AI\u0026#39; and consider where it\u0026#39;s useful and where perhaps there\u0026#39;s a little bit of hype. We also get into some of the technical tradeoffs and decisions that come when building a platform. I\u0026#39;m really excited to get to present this episode to you today as I really enjoyed the discussion.\u003c/p\u003e\u003cp\u003eSpecial Guest: Eric Landau.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Eric Landau (LinkedIn)\" rel=\"nofollow\" href=\"https://www.linkedin.com/in/eric-landau-40992ab0/\"\u003eEric Landau (LinkedIn)\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Encord | The platform for data-centric computer vision\" rel=\"nofollow\" href=\"https://encord.com/\"\u003eEncord | The platform for data-centric computer vision\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Encord blog\" rel=\"nofollow\" href=\"https://blog.encord.com/\"\u003eEncord blog\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Encord (Github)\" rel=\"nofollow\" href=\"https://github.com/encord-team\"\u003eEncord (Github)\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Encord (@encord_team) / Twitter\" rel=\"nofollow\" href=\"https://twitter.com/encord_team\"\u003eEncord (@encord_team) / Twitter\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"This week I spoke with Eric Landau, co-founder of Encord, a platform for data-centric computer vision. This podcast contains a lot of geekery about annotation, and even though Encord aren't an annotation tool per se, Eric and his team have tackled a bunch of quite complicated problems relating to that domain.","date_published":"2022-09-15T10:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/f7d61b52-02c8-4401-894b-92110dde2267.mp3","mime_type":"audio/mpeg","size_in_bytes":37341860,"duration_in_seconds":3111}]},{"id":"38e30182-2cf3-4295-9294-629edca09548","title":"ML Abstractions with Phil Howes","url":"https://podcast.zenml.io/ml-abstractions-phil-howes","content_text":"This week we dive into the abstractions that we're all trying to layer on top of the core ML processes and workflows. I spoke with Phil Howes, co-founder and chief scientist at BaseTen. BaseTen is a platform that allows data scientists to go from an initial model to an MVP web app quickly.\n\nWe got into some of the big challenges he had working to build out the platform, as well as the core issue of iteration speed that motivates why they're building BaseTen.\n\nPhil has experienced quite a few of the industry's end-to-end patterns in the years that he's been working on machine learning and it was great to have that context inform the conversation, too.Special Guest: Phil Howes.Links:Baseten | Turn ML models into full-stack appsWelcome to Baseten! - BasetenBlog | BasetenGallery | Basetenbasetenlabs/truss: Serve any model without boilerplate codeBasetenPhil Howes (LinkedIn)","content_html":"\u003cp\u003eThis week we dive into the abstractions that we\u0026#39;re all trying to layer on top of the core ML processes and workflows. I spoke with Phil Howes, co-founder and chief scientist at BaseTen. BaseTen is a platform that allows data scientists to go from an initial model to an MVP web app quickly.\u003c/p\u003e\n\n\u003cp\u003eWe got into some of the big challenges he had working to build out the platform, as well as the core issue of iteration speed that motivates why they\u0026#39;re building BaseTen.\u003c/p\u003e\n\n\u003cp\u003ePhil has experienced quite a few of the industry\u0026#39;s end-to-end patterns in the years that he\u0026#39;s been working on machine learning and it was great to have that context inform the conversation, too.\u003c/p\u003e\u003cp\u003eSpecial Guest: Phil Howes.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Baseten | Turn ML models into full-stack apps\" rel=\"nofollow\" href=\"https://www.baseten.co/\"\u003eBaseten | Turn ML models into full-stack apps\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Welcome to Baseten! - Baseten\" rel=\"nofollow\" href=\"https://docs.baseten.co/\"\u003eWelcome to Baseten! - Baseten\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Blog | Baseten\" rel=\"nofollow\" href=\"https://www.baseten.co/blog\"\u003eBlog | Baseten\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Gallery | Baseten\" rel=\"nofollow\" href=\"https://www.baseten.co/gallery\"\u003eGallery | Baseten\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"basetenlabs/truss: Serve any model without boilerplate code\" rel=\"nofollow\" href=\"https://github.com/basetenlabs/truss\"\u003ebasetenlabs/truss: Serve any model without boilerplate code\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Baseten\" rel=\"nofollow\" href=\"https://github.com/basetenlabs\"\u003eBaseten\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Phil Howes (LinkedIn)\" rel=\"nofollow\" href=\"https://www.linkedin.com/in/philhowes/\"\u003ePhil Howes (LinkedIn)\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"This week we dive into the abstractions that we're all trying to layer on top of the core ML processes and workflows. I spoke with Phil Howes, co-founder and chief scientist at BaseTen. BaseTen is a platform that allows data scientists to go from an initial model to an MVP web app quickly.","date_published":"2022-09-05T09:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/38e30182-2cf3-4295-9294-629edca09548.mp3","mime_type":"audio/mpeg","size_in_bytes":39044882,"duration_in_seconds":3253}]},{"id":"b5a7fa67-7d9f-46af-a184-bfac8759bae4","title":"Building MLOps Tools with Outerbounds","url":"https://podcast.zenml.io/building-mlops-tools-outerbounds-metaflow","content_text":"This week I spoke with Savin Goyal and Hugo Bowne-Anderson from Outerbounds. They both work on leading, building and helping people put models into production through Metaflow, and I'm sure current users of ZenML will find this conversation interesting to hear how they think through the broader questions and engineering problems involved with MLOps.\n\nAbove all, we spoke about the challenges involved in building a tool that handles the whole machine learning story, from collecting data to training models, to deployment and back again. In many ways it's great that there are lots of smart people thinking about this really hard problem, and even though it is by no means 'solved' conversations like this make me feel cautiously optimistic about the space.Special Guests: Hugo Bowne-Anderson and Savin Goyal.Links:Infrastructure for ML and Data Science | OuterboundsMetaflow Resources for Engineers | OuterboundsMetaflow Resources for Data Science | Outerboundsnbdev+Quarto: A new secret weapon for productivity · fast.ainbdev – Create delightful software with Jupyter NotebooksMetaflowWelcome to Metaflow | Metaflow Docs","content_html":"\u003cp\u003eThis week I spoke with Savin Goyal and Hugo Bowne-Anderson from Outerbounds. They both work on leading, building and helping people put models into production through Metaflow, and I\u0026#39;m sure current users of ZenML will find this conversation interesting to hear how they think through the broader questions and engineering problems involved with MLOps.\u003c/p\u003e\n\n\u003cp\u003eAbove all, we spoke about the challenges involved in building a tool that handles the whole machine learning story, from collecting data to training models, to deployment and back again. In many ways it\u0026#39;s great that there are lots of smart people thinking about this really hard problem, and even though it is by no means \u0026#39;solved\u0026#39; conversations like this make me feel cautiously optimistic about the space.\u003c/p\u003e\u003cp\u003eSpecial Guests: Hugo Bowne-Anderson and Savin Goyal.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Infrastructure for ML and Data Science | Outerbounds\" rel=\"nofollow\" href=\"https://outerbounds.com/\"\u003eInfrastructure for ML and Data Science | Outerbounds\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Metaflow Resources for Engineers | Outerbounds\" rel=\"nofollow\" href=\"https://outerbounds.com/docs/engineering-welcome\"\u003eMetaflow Resources for Engineers | Outerbounds\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Metaflow Resources for Data Science | Outerbounds\" rel=\"nofollow\" href=\"https://outerbounds.com/docs/data-science-welcome\"\u003eMetaflow Resources for Data Science | Outerbounds\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"nbdev+Quarto: A new secret weapon for productivity · fast.ai\" rel=\"nofollow\" href=\"https://www.fast.ai/2022/07/28/nbdev-v2/\"\u003enbdev+Quarto: A new secret weapon for productivity · fast.ai\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"nbdev – Create delightful software with Jupyter Notebooks\" rel=\"nofollow\" href=\"https://nbdev.fast.ai/\"\u003enbdev – Create delightful software with Jupyter Notebooks\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Metaflow\" rel=\"nofollow\" href=\"https://metaflow.org/\"\u003eMetaflow\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Welcome to Metaflow | Metaflow Docs\" rel=\"nofollow\" href=\"https://docs.metaflow.org/\"\u003eWelcome to Metaflow | Metaflow Docs\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"This week I spoke with Savin Goyal and Hugo Bowne-Anderson from Outerbounds. They both work on leading, building and helping people put models into production through Metaflow, and I'm sure current users of ZenML will find this conversation interesting to hear how they think through the broader questions and engineering problems involved with MLOps.","date_published":"2022-08-22T08:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/b5a7fa67-7d9f-46af-a184-bfac8759bae4.mp3","mime_type":"audio/mpeg","size_in_bytes":43007230,"duration_in_seconds":3583}]},{"id":"6300d5ea-04f5-45a5-8c81-ca184b3d5bd4","title":"Safe and Testable Computer Vision with Lakera","url":"https://podcast.zenml.io/safe-testable-computer-vision-lakera","content_text":"This week I spoke with Mateo Rojas-Carulla, the CTO and a co-founder of Lakera and Matthias Kraft, also a co-founder and the CPO there. Lakera is an AI safety company that does a lot of work in the computer vision domain, building a platform and tools for users to gain more confidence in the output and functionality of their models.\n\nWe discuss how they think about the testing of machine learning models, and about how having this safety element upfront has implications for how you go about the testing and ensuring robustness. We specifically dive into how to go about testing computer vision models and the various pitfalls that are to be found in that domain.Special Guests: Mateo Rojas-Carulla and Matthias Kraft.","content_html":"\u003cp\u003eThis week I spoke with Mateo Rojas-Carulla, the CTO and a co-founder of \u003ca href=\"https://www.lakera.ai/\" rel=\"nofollow\"\u003eLakera\u003c/a\u003e and Matthias Kraft, also a co-founder and the CPO there. \u003ca href=\"https://www.lakera.ai/\" rel=\"nofollow\"\u003eLakera\u003c/a\u003e is an AI safety company that does a lot of work in the computer vision domain, building a platform and tools for users to gain more confidence in the output and functionality of their models.\u003c/p\u003e\n\n\u003cp\u003eWe discuss how they think about the testing of machine learning models, and about how having this safety element upfront has implications for how you go about the testing and ensuring robustness. We specifically dive into how to go about testing computer vision models and the various pitfalls that are to be found in that domain.\u003c/p\u003e\u003cp\u003eSpecial Guests: Mateo Rojas-Carulla and Matthias Kraft.\u003c/p\u003e","summary":"This week I spoke with Mateo Rojas-Carulla, the CTO and a co-founder of Lakera and Matthias Kraft, also a co-founder and the CPO there. Lakera is an AI safety company that does a lot of work in the computer vision domain, building a platform and tools for users to gain more confidence in the output and functionality of their models.","date_published":"2022-08-04T10:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/6300d5ea-04f5-45a5-8c81-ca184b3d5bd4.mp3","mime_type":"audio/mpeg","size_in_bytes":41436610,"duration_in_seconds":3452}]},{"id":"bc28e259-b867-43c8-96c4-24d355b78903","title":"Satellite Vision with Robin Cole","url":"https://podcast.zenml.io/satellite-vision-robin-cole","content_text":"This week I spoke with Robin Cole, a senior data scientist at Satellite Vu, a company that's about to launch a thermal imaging satellite into space in order to provide new ways of seeing the earth from above.\n\nRobin generously took the time to discuss his day to day work involving satellite data, the stack they work with at Satellite Vu as well as some of the difficulties that come up in the domain. We also discuss the extremely popular satellite-image-deep-learning GitHub repo that presents resources for those working with or seeking to learn about this kind of data.Special Guest: Robin Cole.Links:About Us — Satellite VuSatellite Vu (LinkedIn)Satellite Vu prepares to launch its thermal imaging satellite constellation with $21M A round | TechCrunchrobmarkcole/satellite-image-deep-learning: Resources for deep learning with satellite \u0026amp; aerial imageryRobin Cole (LinkedIn)GeoTIFF - Wikipedia","content_html":"\u003cp\u003eThis week I spoke with Robin Cole, a senior data scientist at \u003ca href=\"https://www.satellitevu.com\" rel=\"nofollow\"\u003eSatellite Vu\u003c/a\u003e, a company that\u0026#39;s about to launch a thermal imaging satellite into space in order to provide new ways of seeing the earth from above.\u003c/p\u003e\n\n\u003cp\u003eRobin generously took the time to discuss his day to day work involving satellite data, the stack they work with at Satellite Vu as well as some of the difficulties that come up in the domain. We also discuss the extremely popular \u003ca href=\"https://github.com/robmarkcole/satellite-image-deep-learning\" rel=\"nofollow\"\u003esatellite-image-deep-learning GitHub repo\u003c/a\u003e that presents resources for those working with or seeking to learn about this kind of data.\u003c/p\u003e\u003cp\u003eSpecial Guest: Robin Cole.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"About Us — Satellite Vu\" rel=\"nofollow\" href=\"https://www.satellitevu.com/about-us\"\u003eAbout Us — Satellite Vu\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Satellite Vu (LinkedIn)\" rel=\"nofollow\" href=\"https://www.linkedin.com/company/satellitevu/\"\u003eSatellite Vu (LinkedIn)\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Satellite Vu prepares to launch its thermal imaging satellite constellation with $21M A round | TechCrunch\" rel=\"nofollow\" href=\"https://techcrunch.com/2022/03/01/satellite-vu-prepares-to-launch-its-thermal-imaging-satellite-constellation-with-21m-a-round/\"\u003eSatellite Vu prepares to launch its thermal imaging satellite constellation with $21M A round | TechCrunch\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"robmarkcole/satellite-image-deep-learning: Resources for deep learning with satellite \u0026amp; aerial imagery\" rel=\"nofollow\" href=\"https://github.com/robmarkcole/satellite-image-deep-learning\"\u003erobmarkcole/satellite-image-deep-learning: Resources for deep learning with satellite \u0026amp; aerial imagery\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Robin Cole (LinkedIn)\" rel=\"nofollow\" href=\"https://www.linkedin.com/in/robmarkcole/\"\u003eRobin Cole (LinkedIn)\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"GeoTIFF - Wikipedia\" rel=\"nofollow\" href=\"https://en.wikipedia.org/wiki/GeoTIFF\"\u003eGeoTIFF - Wikipedia\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"This week I spoke with Robin Cole, a senior data scientist at Satellite Vu, a company that's about to launch a thermal imaging satellite into space in order to provide new ways of seeing the earth from above.","date_published":"2022-07-28T09:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/bc28e259-b867-43c8-96c4-24d355b78903.mp3","mime_type":"audio/mpeg","size_in_bytes":34519805,"duration_in_seconds":2876}]},{"id":"6c9b65e9-4f39-430e-8cfb-fd17a76fe8d7","title":"Autonomous Shipping with Captain AI","url":"https://podcast.zenml.io/autonomous-shipping-gerard-kruisheer-captain-ai","content_text":"This week on the podcast I spoke with Gerard Kruisheer, the CTO and co-founder of Captain AI, a company based in the Netherlands working on autonomous shipping out of the busy Rotterdam port.\n\nWe discussed the unique problems that come with building autonomous vehicles, the extent to which the latest and greatest research informs their work, their production stack and how they handle deployment for their particular setup.\n\nAs always please let us know if you have guests you'd like me to speak to by sending a message to us on slack or by emailing [podcast@zenml.io](podcast@zenml.io).Special Guest: Gerard Kruisheer.Links:Gerard Kruisheer (LinkedIn profile)Captain AI – Autonomous ships for autonomous portsBlog – Captain AIThe ship which sails itself: arriving soon, thanks to Captain AI and Xsens motion tracking modulesCaptain AI - YouTubeNational Geographic - Captain AI - YouTubeCaptain AI - YouTube","content_html":"\u003cp\u003eThis week on the podcast I spoke with Gerard Kruisheer, the CTO and co-founder of \u003ca href=\"https://www.captainai.com/\" rel=\"nofollow\"\u003eCaptain AI\u003c/a\u003e, a company based in the Netherlands working on autonomous shipping out of the busy Rotterdam port.\u003c/p\u003e\n\n\u003cp\u003eWe discussed the unique problems that come with building autonomous vehicles, the extent to which the latest and greatest research informs their work, their production stack and how they handle deployment for their particular setup.\u003c/p\u003e\n\n\u003cp\u003eAs always please let us know if you have guests you\u0026#39;d like me to speak to by sending a message to us on slack or by emailing [\u003ca href=\"mailto:podcast@zenml.io\" rel=\"nofollow\"\u003epodcast@zenml.io\u003c/a\u003e](\u003ca href=\"mailto:podcast@zenml.io\" rel=\"nofollow\"\u003epodcast@zenml.io\u003c/a\u003e).\u003c/p\u003e\u003cp\u003eSpecial Guest: Gerard Kruisheer.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Gerard Kruisheer (LinkedIn profile)\" rel=\"nofollow\" href=\"https://www.linkedin.com/in/gkruisheer/\"\u003eGerard Kruisheer (LinkedIn profile)\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Captain AI – Autonomous ships for autonomous ports\" rel=\"nofollow\" href=\"https://www.captainai.com/\"\u003eCaptain AI – Autonomous ships for autonomous ports\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Blog – Captain AI\" rel=\"nofollow\" href=\"https://www.captainai.com/blog/\"\u003eBlog – Captain AI\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"The ship which sails itself: arriving soon, thanks to Captain AI and Xsens motion tracking modules\" rel=\"nofollow\" href=\"https://www.xsens.com/cases/the-ship-which-sails-itself-arriving-soon-thanks-to-captain-ai-and-xsens-motion-tracking-modules\"\u003eThe ship which sails itself: arriving soon, thanks to Captain AI and Xsens motion tracking modules\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Captain AI - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/channel/UC4vsUMnLs06MfApE4GOBp5w\"\u003eCaptain AI - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"National Geographic - Captain AI - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=sLr_NhnYI88\"\u003eNational Geographic - Captain AI - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Captain AI - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=3Hg7iNaa-GA\"\u003eCaptain AI - YouTube\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"This week on the podcast I spoke with Gerard Kruisheer, the CTO and co-founder of Captain AI, a company based in the Netherlands working on autonomous shipping out of the busy Rotterdam port.","date_published":"2022-07-21T10:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/6c9b65e9-4f39-430e-8cfb-fd17a76fe8d7.mp3","mime_type":"audio/mpeg","size_in_bytes":43466559,"duration_in_seconds":3622}]},{"id":"57e441f1-021f-42f6-b676-fd8077e4eca1","title":"ML Monitoring with Emeli Dral","url":"https://podcast.zenml.io/monitoring-evidently-emeli-dral","content_text":"I'll be having some conversations with the people behind the tools that ZenML offers as integrations. We spoke with Ben Wilson a few weeks back, and today I'm pleased to publish this conversation with Emeli Dral, co-founder and CTO of Evidently, an open-source tool tackling the problem of monitoring of models and data for machine learning.\n\nWe discussed the challenges around building a tool that is both straightforward to use while also customisable and powerful. We also got into the thinking behind how they grew their community and blog along the way.Special Guest: Emeli Dral.Links:Emeli Dral (LinkedIn)Emeli Dral (@EmeliDral) / TwitterEvidently AI - Open-Source Machine Learning MonitoringEvidently DocumentationEvidently AI Blog - Machine Learning in ProductionEvidently AI - Community \u0026amp; SupportEmeli Dral - How Your ML Model Will Fail and How to Prepare for It - YouTubeEmeli Dral: The day after deployment: how to set up your model monitoring - YouTubeIs My Data Drifting? Early Monitoring for Machine Learning Models in Production | PyData Global 2021 - YouTubeMonitoring Machine Learning Systems in Production - YouTube","content_html":"\u003cp\u003eI\u0026#39;ll be having some conversations with the people behind the tools that ZenML offers as integrations. We spoke with Ben Wilson a few weeks back, and today I\u0026#39;m pleased to publish this conversation with Emeli Dral, co-founder and CTO of Evidently, an open-source tool tackling the problem of monitoring of models and data for machine learning.\u003c/p\u003e\n\n\u003cp\u003eWe discussed the challenges around building a tool that is both straightforward to use while also customisable and powerful. We also got into the thinking behind how they grew their community and blog along the way.\u003c/p\u003e\u003cp\u003eSpecial Guest: Emeli Dral.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Emeli Dral (LinkedIn)\" rel=\"nofollow\" href=\"https://www.linkedin.com/in/emelidral/\"\u003eEmeli Dral (LinkedIn)\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Emeli Dral (@EmeliDral) / Twitter\" rel=\"nofollow\" href=\"https://twitter.com/EmeliDral\"\u003eEmeli Dral (@EmeliDral) / Twitter\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Evidently AI - Open-Source Machine Learning Monitoring\" rel=\"nofollow\" href=\"https://evidentlyai.com/\"\u003eEvidently AI - Open-Source Machine Learning Monitoring\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Evidently Documentation\" rel=\"nofollow\" href=\"https://docs.evidentlyai.com/\"\u003eEvidently Documentation\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Evidently AI Blog - Machine Learning in Production\" rel=\"nofollow\" href=\"https://evidentlyai.com/blog\"\u003eEvidently AI Blog - Machine Learning in Production\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Evidently AI - Community \u0026amp; Support\" rel=\"nofollow\" href=\"https://evidentlyai.com/community\"\u003eEvidently AI - Community \u0026amp; Support\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Emeli Dral - How Your ML Model Will Fail and How to Prepare for It - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=35A7P03wA0Y\"\u003eEmeli Dral - How Your ML Model Will Fail and How to Prepare for It - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Emeli Dral: The day after deployment: how to set up your model monitoring - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=vTkQHLLX3rQ\"\u003eEmeli Dral: The day after deployment: how to set up your model monitoring - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Is My Data Drifting? Early Monitoring for Machine Learning Models in Production | PyData Global 2021 - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=ukWc6mv8ojw\"\u003eIs My Data Drifting? Early Monitoring for Machine Learning Models in Production | PyData Global 2021 - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Monitoring Machine Learning Systems in Production - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=Z8b64sgTmaU\"\u003eMonitoring Machine Learning Systems in Production - YouTube\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"I'll be having some conversations with the people behind the tools that ZenML offers as integrations. We spoke with Ben Wilson a few weeks back, and today I'm pleased to publish this conversation with Emeli Dral, co-founder and CTO of Evidently, an open-source tool tackling the problem of monitoring of models and data for machine learning.","date_published":"2022-07-07T11:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/57e441f1-021f-42f6-b676-fd8077e4eca1.mp3","mime_type":"audio/mpeg","size_in_bytes":33808242,"duration_in_seconds":2817}]},{"id":"2f68e4a6-373b-416d-86be-327ac4f52ab4","title":"Edge Computer Vision with Karthik Kannan","url":"https://podcast.zenml.io/edge-computer-vision-karthik-kannan","content_text":"This week I spoke with Karthik Kannan, cofounder and CTO of Envision, a company that builds on top of the Google Glass and using Augmented Reality features of phones to allow visually impaired people to better sense the environment or objects around them.\n\nTheir software and devices are pretty popular and as you'll hear in this conversation, they've been on a real journey to get to where they are now.\n\nIn particular, I really enjoyed the parts where Karthik explained their development and deployment process in detail. It's not too often that you get a deep dive into the workflows and stacks of an embedded computer vision company and tool and so I think you're going to really enjoy this one.Special Guest: Karthik Kannan.Links:Karthik Kannan (@meTheKarthik) / TwitterEnvision - Hear what you want to see.Glass – GlassIntroducing Envision Glasses: AI-powered smartglasses for the Blind \u0026amp; Visually Impaired - YouTubeEnvision Blog","content_html":"\u003cp\u003eThis week I spoke with Karthik Kannan, cofounder and CTO of \u003ca href=\"https://www.letsenvision.com/\" rel=\"nofollow\"\u003eEnvision\u003c/a\u003e, a company that builds on top of the Google Glass and using Augmented Reality features of phones to allow visually impaired people to better sense the environment or objects around them.\u003c/p\u003e\n\n\u003cp\u003eTheir software and devices are pretty popular and as you\u0026#39;ll hear in this conversation, they\u0026#39;ve been on a real journey to get to where they are now.\u003c/p\u003e\n\n\u003cp\u003eIn particular, I really enjoyed the parts where Karthik explained their development and deployment process in detail. It\u0026#39;s not too often that you get a deep dive into the workflows and stacks of an embedded computer vision company and tool and so I think you\u0026#39;re going to really enjoy this one.\u003c/p\u003e\u003cp\u003eSpecial Guest: Karthik Kannan.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Karthik Kannan (@meTheKarthik) / Twitter\" rel=\"nofollow\" href=\"https://twitter.com/meTheKarthik\"\u003eKarthik Kannan (@meTheKarthik) / Twitter\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Envision - Hear what you want to see.\" rel=\"nofollow\" href=\"https://www.letsenvision.com/\"\u003eEnvision - Hear what you want to see.\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Glass – Glass\" rel=\"nofollow\" href=\"https://www.google.com/glass/start/\"\u003eGlass – Glass\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Introducing Envision Glasses: AI-powered smartglasses for the Blind \u0026amp; Visually Impaired - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=9ehENnq2EFo\"\u003eIntroducing Envision Glasses: AI-powered smartglasses for the Blind \u0026amp; Visually Impaired - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Envision Blog\" rel=\"nofollow\" href=\"https://www.letsenvision.com/blog\"\u003eEnvision Blog\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"This week I spoke with Karthik Kannan, cofounder and CTO of Envision, a company that builds on top of the Google Glass and using Augmented Reality features of phones to allow visually impaired people to better sense the environment or objects around them.","date_published":"2022-06-30T09:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/2f68e4a6-373b-416d-86be-327ac4f52ab4.mp3","mime_type":"audio/mpeg","size_in_bytes":33765004,"duration_in_seconds":2813}]},{"id":"2b5720f5-ce03-4cd5-a077-87780513ee1d","title":"Humans in the Loop with Iva Gumnishka","url":"https://podcast.zenml.io/humans-in-loop-iva-gumnishka","content_text":"In this episode, I'm really happy to be able to continue the dialogue we've been having with our users and community around the role of data annotation and labeling in MLOps.\n\nWe were lucky to get to talk to Iva Gumnishka, the founder of Humans in the Loop. They are an organisation that provides data annotation and collection services. Their teams are primarily made up of those who have been affected by conflict and now are asylum seekers or refugees.\n\nIva has a ton of experience working with annotation and has seen how different companies build this into their production machine learning lifecycles. We're continuing to work on a feature that will allow you to do this as part of your MLOps workflow when using ZenML, and I welcome any feedback you might have on the back of this podcast or the articles we've been publishing on the ZenML blog.Special Guest: Iva Gumnishka.Links:Humans in the Loop | Image annotation for ethical AIBlog | Humans in the Loop10 of the best open-source annotation tools for computer vision 2022 | Humans in the Loopzenml-io/awesome-open-data-annotation: Open Source Data Annotation \u0026amp; Labeling ToolsNeed an open-source data annotation tool? We’ve got you covered! | ZenML BlogHow to get the most out of data annotation | ZenML BlogFoundation | Humans in the LoopYour Data Needs a Human Touch. The story of Iva Gumnishka, a Bulgarian… | by Antoaneta Manko | womenintechglobal | Medium","content_html":"\u003cp\u003eIn this episode, I\u0026#39;m really happy to be able to continue the dialogue we\u0026#39;ve been having with our users and community around the role of data annotation and labeling in MLOps.\u003c/p\u003e\n\n\u003cp\u003eWe were lucky to get to talk to \u003ca href=\"https://www.linkedin.com/in/ivagumnishka/\" rel=\"nofollow\"\u003eIva Gumnishka\u003c/a\u003e, the founder of \u003ca href=\"https://humansintheloop.org/\" rel=\"nofollow\"\u003eHumans in the Loop\u003c/a\u003e. They are an organisation that provides data annotation and collection services. Their teams are primarily made up of those who have been affected by conflict and now are asylum seekers or refugees.\u003c/p\u003e\n\n\u003cp\u003eIva has a ton of experience working with annotation and has seen how different companies build this into their production machine learning lifecycles. We\u0026#39;re continuing to work on a feature that will allow you to do this as part of your MLOps workflow when using ZenML, and I welcome any feedback you might have on the back of this podcast or the articles we\u0026#39;ve been publishing on the ZenML blog.\u003c/p\u003e\u003cp\u003eSpecial Guest: Iva Gumnishka.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Humans in the Loop | Image annotation for ethical AI\" rel=\"nofollow\" href=\"https://humansintheloop.org/\"\u003eHumans in the Loop | Image annotation for ethical AI\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Blog | Humans in the Loop\" rel=\"nofollow\" href=\"https://humansintheloop.org/resources/blog/\"\u003eBlog | Humans in the Loop\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"10 of the best open-source annotation tools for computer vision 2022 | Humans in the Loop\" rel=\"nofollow\" href=\"https://humansintheloop.org/10-of-the-best-open-source-annotation-tools-for-computer-vision-2022/\"\u003e10 of the best open-source annotation tools for computer vision 2022 | Humans in the Loop\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"zenml-io/awesome-open-data-annotation: Open Source Data Annotation \u0026amp; Labeling Tools\" rel=\"nofollow\" href=\"https://github.com/zenml-io/awesome-open-data-annotation\"\u003ezenml-io/awesome-open-data-annotation: Open Source Data Annotation \u0026amp; Labeling Tools\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Need an open-source data annotation tool? We’ve got you covered! | ZenML Blog\" rel=\"nofollow\" href=\"https://blog.zenml.io/open-source-data-annotation-tools/\"\u003eNeed an open-source data annotation tool? We’ve got you covered! | ZenML Blog\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"How to get the most out of data annotation | ZenML Blog\" rel=\"nofollow\" href=\"https://blog.zenml.io/data-labelling-annotation/\"\u003eHow to get the most out of data annotation | ZenML Blog\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Foundation | Humans in the Loop\" rel=\"nofollow\" href=\"https://humansintheloop.org/why-us/foundation/\"\u003eFoundation | Humans in the Loop\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Your Data Needs a Human Touch. The story of Iva Gumnishka, a Bulgarian… | by Antoaneta Manko | womenintechglobal | Medium\" rel=\"nofollow\" href=\"https://medium.com/bulgarianwomenintech/your-data-needs-a-human-touch-5bc2ee70d548\"\u003eYour Data Needs a Human Touch. The story of Iva Gumnishka, a Bulgarian… | by Antoaneta Manko | womenintechglobal | Medium\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"We were lucky to get to talk to [Iva Gumnishka](https://www.linkedin.com/in/ivagumnishka/), the founder of [Humans in the Loop](https://humansintheloop.org/). They are an organisation that provides data annotation and collection services. Their teams are primarily made up of those who have been affected by conflict and now are asylum seekers or refugees.","date_published":"2022-06-23T10:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/2b5720f5-ce03-4cd5-a077-87780513ee1d.mp3","mime_type":"audio/mpeg","size_in_bytes":36662221,"duration_in_seconds":3055}]},{"id":"7d3547bd-c623-47fb-ad0b-c59658a40296","title":"ML Engineering with Ben Wilson","url":"https://podcast.zenml.io/ml-engineering-ben-wilson","content_text":"We took a few weeks break to reach out to some new guests and so I think we can go so far as declaring this next series of episodes as season 2 of Pipeline Conversations.\n\nToday, I'm extremely excited to present this conversation I had with Ben Wilson who works over at Databricks and who has also just released a new book called 'Machine Learning Engineering in Action'. It's a jam-backed guide to all the lessons that Ben has learned over his years working to help companies get models out into the world and run them in production.\n\nI was really lucky to get to talk to Ben about his new book and also about the mental models he thinks are useful to bring to bear on this complicated problem many of us are working on.Special Guest: Ben Wilson.Links:Ben Wilson (LinkedIn)Adventures in Machine Learning (podcast)Machine Learning Engineering in Action (Manning book)Databricks","content_html":"\u003cp\u003eWe took a few weeks break to reach out to some new guests and so I think we can go so far as declaring this next series of episodes as season 2 of Pipeline Conversations.\u003c/p\u003e\n\n\u003cp\u003eToday, I\u0026#39;m extremely excited to present this conversation I had with Ben Wilson who works over at Databricks and who has also just released a new book called \u0026#39;\u003ca href=\"https://www.manning.com/books/machine-learning-engineering-in-action\" rel=\"nofollow\"\u003eMachine Learning Engineering in Action\u003c/a\u003e\u0026#39;. It\u0026#39;s a jam-backed guide to all the lessons that Ben has learned over his years working to help companies get models out into the world and run them in production.\u003c/p\u003e\n\n\u003cp\u003eI was really lucky to get to talk to Ben about his new book and also about the mental models he thinks are useful to bring to bear on this complicated problem many of us are working on.\u003c/p\u003e\u003cp\u003eSpecial Guest: Ben Wilson.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Ben Wilson (LinkedIn)\" rel=\"nofollow\" href=\"https://www.linkedin.com/in/benjamin-wilson-arch\"\u003eBen Wilson (LinkedIn)\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Adventures in Machine Learning (podcast)\" rel=\"nofollow\" href=\"https://adventuresinml.com/\"\u003eAdventures in Machine Learning (podcast)\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Machine Learning Engineering in Action (Manning book)\" rel=\"nofollow\" href=\"https://www.manning.com/books/machine-learning-engineering-in-action\"\u003eMachine Learning Engineering in Action (Manning book)\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Databricks\" rel=\"nofollow\" href=\"https://databricks.com/\"\u003eDatabricks\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"Today, I'm extremely excited to present this conversation I had with Ben Wilson who works over at Databricks and who has also just released a new book called 'Machine Learning Engineering in Action'.","date_published":"2022-06-08T16:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/7d3547bd-c623-47fb-ad0b-c59658a40296.mp3","mime_type":"audio/mpeg","size_in_bytes":46579714,"duration_in_seconds":3881}]},{"id":"8ce789d5-23c4-4251-933d-c4797ea40684","title":"ZenML Recap with Adam and Hamza","url":"https://podcast.zenml.io/zenml-recap-adam-hamza","content_text":"Adam and Hamza return for a short discussion of what we've been busy working on during the previous few months, where we're going with ZenML and why it's so amazing to be building an open-source tool.","content_html":"\u003cp\u003eAdam and Hamza return for a short discussion of what we\u0026#39;ve been busy working on during the previous few months, where we\u0026#39;re going with ZenML and why it\u0026#39;s so amazing to be building an open-source tool.\u003c/p\u003e","summary":"Adam and Hamza return for a short discussion of what we've been busy working on during the previous few months, where we're going with ZenML and why it's so amazing to be building an open-source tool.","date_published":"2022-04-28T12:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/8ce789d5-23c4-4251-933d-c4797ea40684.mp3","mime_type":"audio/mpeg","size_in_bytes":18373251,"duration_in_seconds":1531}]},{"id":"3b306917-5653-40d1-b3c7-85c92ac80ad3","title":"Trustworthy ML with Kush Varshney","url":"https://podcast.zenml.io/trustworthy-ml-kush-varshney","content_text":"I enthusiastically read Kush Varshney's book when it was released for free to the world several months back. Trustworthy Machine Learning is a concise and clear overview of many of the ways that machine learning can go wrong, and so I was especially keen to get Kush on to talk more about his work and research.\n\nI also got a stronger sense of appreciation for how good MLOps practices and workflows offered a clear path to ensuring that your machine learning models and behaviours could become more trustworthy. Kush has done a lot of interesting work, particularly with the AI Fairness 360 and AI Explainability 360 toolkits that I'm sure listeners of this podcast would find worth checking out.Special Guest: Kush Varshney.Links:Trustworthy Machine Learning by Kush R. VarshneyHome - AI Explainability 360Home - AI Fairness 360Kush VarshneyKush Varshney (@krvarshney) / TwitterKush Varshney | LinkedInTrustworthy Machine Learning: Varshney, Kush R.: 9798411903959: Amazon.com: Books","content_html":"\u003cp\u003eI enthusiastically read Kush Varshney\u0026#39;s book when it was released for free to the world several months back. \u003ca href=\"http://www.trustworthymachinelearning.com/\" rel=\"nofollow\"\u003eTrustworthy Machine Learning\u003c/a\u003e is a concise and clear overview of many of the ways that machine learning can go wrong, and so I was especially keen to get \u003ca href=\"http://krvarshney.github.io/\" rel=\"nofollow\"\u003eKush\u003c/a\u003e on to talk more about his work and research.\u003c/p\u003e\n\n\u003cp\u003eI also got a stronger sense of appreciation for how good MLOps practices and workflows offered a clear path to ensuring that your machine learning models and behaviours could become more trustworthy. Kush has done a lot of interesting work, particularly with the \u003ca href=\"https://ai-fairness-360.org/\" rel=\"nofollow\"\u003eAI Fairness 360\u003c/a\u003e and \u003ca href=\"https://ai-explainability-360.org/\" rel=\"nofollow\"\u003eAI Explainability 360\u003c/a\u003e toolkits that I\u0026#39;m sure listeners of this podcast would find worth checking out.\u003c/p\u003e\u003cp\u003eSpecial Guest: Kush Varshney.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Trustworthy Machine Learning by Kush R. Varshney\" rel=\"nofollow\" href=\"http://www.trustworthymachinelearning.com/\"\u003eTrustworthy Machine Learning by Kush R. Varshney\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Home - AI Explainability 360\" rel=\"nofollow\" href=\"https://ai-explainability-360.org/\"\u003eHome - AI Explainability 360\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Home - AI Fairness 360\" rel=\"nofollow\" href=\"https://ai-fairness-360.org/\"\u003eHome - AI Fairness 360\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Kush Varshney\" rel=\"nofollow\" href=\"http://krvarshney.github.io/\"\u003eKush Varshney\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Kush Varshney (@krvarshney) / Twitter\" rel=\"nofollow\" href=\"https://twitter.com/krvarshney\"\u003eKush Varshney (@krvarshney) / Twitter\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Kush Varshney | LinkedIn\" rel=\"nofollow\" href=\"https://www.linkedin.com/in/kushvarshney/\"\u003eKush Varshney | LinkedIn\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Trustworthy Machine Learning: Varshney, Kush R.: 9798411903959: Amazon.com: Books\" rel=\"nofollow\" href=\"https://www.amazon.com/Trustworthy-Machine-Learning-Kush-Varshney/dp/B09SL5GPCD\"\u003eTrustworthy Machine Learning: Varshney, Kush R.: 9798411903959: Amazon.com: Books\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"I enthusiastically read Kush Varshney's book when it was released for free to the world several months back. Trustworthy Machine Learning is a concise and clear overview of many of the ways that machine learning can go wrong, and so I was especially keen to get Kush on to talk more about his work and research.","date_published":"2022-04-14T11:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/3b306917-5653-40d1-b3c7-85c92ac80ad3.mp3","mime_type":"audio/mpeg","size_in_bytes":28178346,"duration_in_seconds":2348}]},{"id":"3d58b3bb-2933-41cd-a32f-0c59343c894a","title":"Open-Source MLOps with Matt Squire","url":"https://podcast.zenml.io/open-source-mlops-matt-squire","content_text":"This week I spoke with Matt Squire, the CTO and co-founder of Fuzzy Labs, where they help partner organisations think through how best to productionise their machine learning workflows.\n\nMatt and FuzzyLabs are also behind the Awesome Open Source MLOps GitHub repo where you can find all the options for an open-source MLOps stack of your dreams.\n\nMatt has been an enthusiastic early supporter of the work we do at ZenML so it was really amazing to get to talk to him and  get his take based on the many experiences he's had seeing how ML is done out in the field.Special Guest: Matt Squire.Links:Matt Squire | LinkedInOpen Source MLOps - Fuzzy Labsfuzzylabs/awesome-open-mlops: The Fuzzy Labs guide to the universe of open source MLOpsEvidently AI - Open-Source Machine Learning MonitoringData Version Control · DVCBlog - Fuzzy LabsThe Road to Zen: getting started with pipelines - Fuzzy LabsThe Road to Zen: running experiments - Fuzzy LabsGuides to MLOps - Fuzzy Labs","content_html":"\u003cp\u003eThis week I spoke with Matt Squire, the CTO and co-founder of \u003ca href=\"https://www.fuzzylabs.ai\" rel=\"nofollow\"\u003eFuzzy Labs\u003c/a\u003e, where they help partner organisations think through how best to productionise their machine learning workflows.\u003c/p\u003e\n\n\u003cp\u003eMatt and FuzzyLabs are also behind the \u003ca href=\"https://github.com/fuzzylabs/awesome-open-mlops\" rel=\"nofollow\"\u003eAwesome Open Source MLOps\u003c/a\u003e GitHub repo where you can find all the options for an open-source MLOps stack of your dreams.\u003c/p\u003e\n\n\u003cp\u003eMatt has been an enthusiastic early supporter of the work we do at ZenML so it was really amazing to get to talk to him and  get his take based on the many experiences he\u0026#39;s had seeing how ML is done out in the field.\u003c/p\u003e\u003cp\u003eSpecial Guest: Matt Squire.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Matt Squire | LinkedIn\" rel=\"nofollow\" href=\"https://www.linkedin.com/in/matt-squire-a19896125/\"\u003eMatt Squire | LinkedIn\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Open Source MLOps - Fuzzy Labs\" rel=\"nofollow\" href=\"https://www.fuzzylabs.ai/\"\u003eOpen Source MLOps - Fuzzy Labs\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"fuzzylabs/awesome-open-mlops: The Fuzzy Labs guide to the universe of open source MLOps\" rel=\"nofollow\" href=\"https://github.com/fuzzylabs/awesome-open-mlops\"\u003efuzzylabs/awesome-open-mlops: The Fuzzy Labs guide to the universe of open source MLOps\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Evidently AI - Open-Source Machine Learning Monitoring\" rel=\"nofollow\" href=\"https://evidentlyai.com/\"\u003eEvidently AI - Open-Source Machine Learning Monitoring\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Data Version Control · DVC\" rel=\"nofollow\" href=\"https://dvc.org/\"\u003eData Version Control · DVC\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Blog - Fuzzy Labs\" rel=\"nofollow\" href=\"https://www.fuzzylabs.ai/blog\"\u003eBlog - Fuzzy Labs\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"The Road to Zen: getting started with pipelines - Fuzzy Labs\" rel=\"nofollow\" href=\"https://www.fuzzylabs.ai/blog-post/the-road-to-zen-part-1-getting-started-pipelines\"\u003eThe Road to Zen: getting started with pipelines - Fuzzy Labs\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"The Road to Zen: running experiments - Fuzzy Labs\" rel=\"nofollow\" href=\"https://www.fuzzylabs.ai/blog-post/the-road-to-zen-running-experiments\"\u003eThe Road to Zen: running experiments - Fuzzy Labs\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Guides to MLOps - Fuzzy Labs\" rel=\"nofollow\" href=\"https://www.fuzzylabs.ai/guides\"\u003eGuides to MLOps - Fuzzy Labs\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"This week I spoke with Matt Squire, the CTO and co-founder of Fuzzy Labs, where they help partner organisations think through how best to productionise their machine learning workflows.","date_published":"2022-03-31T11:00:00.000+02:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/3d58b3bb-2933-41cd-a32f-0c59343c894a.mp3","mime_type":"audio/mpeg","size_in_bytes":34335915,"duration_in_seconds":2861}]},{"id":"4b687c82-9d24-4df0-9831-06c8704924c1","title":"Practical Production ML with Emmanuel Ameisen","url":"https://podcast.zenml.io/ml-powered-emmanuel-ameisen","content_text":"This week I spoke with Emmanuel Ameisen, a data scientist and ML engineer currently based at Stripe. Emmanuel also wrote an excellent O'Reilly book called \"Building Machine Learning Powered Applications\", a book I find myself often returning to for inspiration and that I was pleased to get the chance to reread in preparation for our discussion.\n\nEmmanuel has previously worked at Insight Data Science where he was involved in mentoring and guiding dozens of data scientists who were working on building their ML portfolio projects. He brings a wealth of experience to the table and I'm really excited to present our conversation to you.Special Guest: Emmanuel Ameisen.Links:Emmanuel Ameisen (@mlpowered) / TwitterEmmanuel Ameisen | LinkedIn — ML Engineer at Stripe, years of experience in Data Science.\r\n\r\nAuthor of Building Machine Learning Powered Applications published by O’Reilly (bit.ly/mlpowered).\r\n\r\nPreviously Head of AI at Insight where I led over 100 applied ML projects.AI in Industry - Lessons from 50+ Companies and Example Projects - YouTubeContinuous Deployment of Critical ML ApplicationsBuilding Machine Learning Powered Applications: Going from Idea to Product: Ameisen, Emmanuel: 9781492045113: Amazon.com: Booksfast.ai · Making neural nets uncool again","content_html":"\u003cp\u003eThis week I spoke with Emmanuel Ameisen, a data scientist and ML engineer currently based at Stripe. Emmanuel also wrote an excellent O\u0026#39;Reilly book called \u0026quot;Building Machine Learning Powered Applications\u0026quot;, a book I find myself often returning to for inspiration and that I was pleased to get the chance to reread in preparation for our discussion.\u003c/p\u003e\n\n\u003cp\u003eEmmanuel has previously worked at Insight Data Science where he was involved in mentoring and guiding dozens of data scientists who were working on building their ML portfolio projects. He brings a wealth of experience to the table and I\u0026#39;m really excited to present our conversation to you.\u003c/p\u003e\u003cp\u003eSpecial Guest: Emmanuel Ameisen.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Emmanuel Ameisen (@mlpowered) / Twitter\" rel=\"nofollow\" href=\"https://twitter.com/mlpowered\"\u003eEmmanuel Ameisen (@mlpowered) / Twitter\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Emmanuel Ameisen | LinkedIn\" rel=\"nofollow\" href=\"https://www.linkedin.com/in/ameisen/\"\u003eEmmanuel Ameisen | LinkedIn\u003c/a\u003e \u0026mdash; ML Engineer at Stripe, years of experience in Data Science.\r\n\r\nAuthor of Building Machine Learning Powered Applications published by O’Reilly (bit.ly/mlpowered).\r\n\r\nPreviously Head of AI at Insight where I led over 100 applied ML projects.\u003c/li\u003e\u003cli\u003e\u003ca title=\"AI in Industry - Lessons from 50+ Companies and Example Projects - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=J31OV3zoQCc\"\u003eAI in Industry - Lessons from 50+ Companies and Example Projects - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Continuous Deployment of Critical ML Applications\" rel=\"nofollow\" href=\"https://mlops.community/watch/continuous-deployment-of-critical-ml-applications_NFLEzw4RIzti6J/\"\u003eContinuous Deployment of Critical ML Applications\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Building Machine Learning Powered Applications: Going from Idea to Product: Ameisen, Emmanuel: 9781492045113: Amazon.com: Books\" rel=\"nofollow\" href=\"https://www.amazon.com/Building-Machine-Learning-Powered-Applications/dp/149204511X?tag=soumet-20\"\u003eBuilding Machine Learning Powered Applications: Going from Idea to Product: Ameisen, Emmanuel: 9781492045113: Amazon.com: Books\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"fast.ai · Making neural nets uncool again\" rel=\"nofollow\" href=\"https://www.fast.ai/\"\u003efast.ai · Making neural nets uncool again\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"This week I spoke with Emmanuel Ameisen, a data scientist and ML engineer currently based at Stripe. Emmanuel also wrote an excellent O'Reilly book called \"Building Machine Learning Powered Applications\", a book I find myself often returning to for inspiration and that I was pleased to get the chance to reread in preparation for our discussion.","date_published":"2022-03-17T17:00:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/4b687c82-9d24-4df0-9831-06c8704924c1.mp3","mime_type":"audio/mpeg","size_in_bytes":41769446,"duration_in_seconds":3480}]},{"id":"8a260b8c-1b7b-4273-b843-925d62537f53","title":"From Academia to Industry with Johnny Greco","url":"https://podcast.zenml.io/academia-to-industry-johnny-greco","content_text":"This week I spoke with Johnny Greco, a data scientist working at Radiology Partners. Johnny transitioned into his current work from a career as an academic — working in astronomy — where also worked in the open-source space to build a really interesting synthetic image data project. \n\nWe get into that project in our conversation but we also discuss his experience of crossing over into industry, the skills that have served him in his new job, and his experience of working in a world where the stakes around models in production are much higher.Special Guest: Johnny Greco.Links:Johnny Grecojohnnygreco (Johnny Greco)Johnny Greco (@johnnypgreco) / TwitterArtPop — ArtPop documentation‪Johnny P Greco‬ - ‪Google Scholar‬johnnygreco/love-thy-pixels: Spreading the love for galaxies one pixel at a timeJohnny Greco: A New View of Low Surface Brightness Galaxies from the Hyper Suprime-Cam Survey - YouTube","content_html":"\u003cp\u003eThis week I spoke with \u003ca href=\"https://johnnygreco.space\" rel=\"nofollow\"\u003eJohnny Greco\u003c/a\u003e, a data scientist working at Radiology Partners. Johnny transitioned into his current work from a career as an academic — working in astronomy — where also worked in the open-source space to build a really interesting synthetic image data project. \u003c/p\u003e\n\n\u003cp\u003eWe get into that project in our conversation but we also discuss his experience of crossing over into industry, the skills that have served him in his new job, and his experience of working in a world where the stakes around models in production are much higher.\u003c/p\u003e\u003cp\u003eSpecial Guest: Johnny Greco.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Johnny Greco\" rel=\"nofollow\" href=\"https://johnnygreco.space/\"\u003eJohnny Greco\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"johnnygreco (Johnny Greco)\" rel=\"nofollow\" href=\"https://github.com/johnnygreco\"\u003ejohnnygreco (Johnny Greco)\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Johnny Greco (@johnnypgreco) / Twitter\" rel=\"nofollow\" href=\"https://twitter.com/johnnypgreco\"\u003eJohnny Greco (@johnnypgreco) / Twitter\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"ArtPop — ArtPop documentation\" rel=\"nofollow\" href=\"https://artpop.readthedocs.io/en/latest/\"\u003eArtPop — ArtPop documentation\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"‪Johnny P Greco‬ - ‪Google Scholar‬\" rel=\"nofollow\" href=\"https://scholar.google.com/citations?user=CDWpgoAAAAAJ\"\u003e‪Johnny P Greco‬ - ‪Google Scholar‬\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"johnnygreco/love-thy-pixels: Spreading the love for galaxies one pixel at a time\" rel=\"nofollow\" href=\"https://github.com/johnnygreco/love-thy-pixels\"\u003ejohnnygreco/love-thy-pixels: Spreading the love for galaxies one pixel at a time\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Johnny Greco: A New View of Low Surface Brightness Galaxies from the Hyper Suprime-Cam Survey - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=ZcNFt0LNUIw\"\u003eJohnny Greco: A New View of Low Surface Brightness Galaxies from the Hyper Suprime-Cam Survey - YouTube\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"This week I spoke with Johnny Greco, a data scientist working at Radiology Partners. Johnny transitioned into his current work from a career as an academic — working in astronomy — where also worked in the open-source space to build a really interesting synthetic image data project. \r\n\r\nWe get into that project in our conversation but we also discuss his experience of crossing over into industry, the skills that have served him in his new job, and his experience of working in a world where the stakes around models in production are much higher.","date_published":"2022-03-03T17:00:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/8a260b8c-1b7b-4273-b843-925d62537f53.mp3","mime_type":"audio/mpeg","size_in_bytes":40736388,"duration_in_seconds":3394}]},{"id":"514366af-956a-4991-aa40-90be4f4c153a","title":"The Modern Data Stack with Tristan Zajonc","url":"https://podcast.zenml.io/modern-data-stack-tristan-zajonc","content_text":"This week I spoke with Tristan Zajonc, the CEO and cofounder of Continual, a company that provides an AI layer for enterprise companies or, as we'll get into in the podcast, the so-called 'modern data stack'.\n\nHe previously worked at Cloudera as a CTO for machine learning and as the head of the data science platform there, and he holds a PhD in public policy from Harvard University.\n\nIn our conversation we discussed the different levels of abstraction one can take when dealing with the MLOps problem. We spoke about all the different ways that machine learning can fail in production settings and of course we discussed the concept of the 'modern data stack' and what that means.Special Guest: Tristan Zajonc.Links:Tristan Zajonc (@tristanzajonc) / TwitterTristan Zajonc | LinkedInContinual | AI/ML for Your Cloud Data WarehouseCompany | Continual - Operational AI for the EnterpriseThe Modern Data Stack Ecosystem - Fall 2021 EditionThe Future of the Modern Data StackIntroducing Continual – the missing AI layer for the modern data stackCloudera | The Hybrid Data Cloud CompanyTristan Zajonc, Sense Platform // Data Driven #28 // June 2014 (Hosted by FirstMark Capital) - YouTubeSense Preview - YouTubeDC_THURS on Operational AI for the Modern Data Stack w/ Tristan Zajonc (Continual) - YouTubeEnterprise Machine Learning on K8s: Lessons Learned and the Road... - Timothy Chen \u0026amp; Tristan Zajonc - YouTube","content_html":"\u003cp\u003eThis week I spoke with \u003ca href=\"https://www.linkedin.com/in/tristanzajonc/\" rel=\"nofollow\"\u003eTristan Zajonc\u003c/a\u003e, the CEO and cofounder of \u003ca href=\"https://continual.ai/\" rel=\"nofollow\"\u003eContinual\u003c/a\u003e, a company that provides an AI layer for enterprise companies or, as we\u0026#39;ll get into in the podcast, the so-called \u0026#39;modern data stack\u0026#39;.\u003c/p\u003e\n\n\u003cp\u003eHe previously worked at Cloudera as a CTO for machine learning and as the head of the data science platform there, and he holds a PhD in public policy from Harvard University.\u003c/p\u003e\n\n\u003cp\u003eIn our conversation we discussed the different levels of abstraction one can take when dealing with the MLOps problem. We spoke about all the different ways that machine learning can fail in production settings and of course we discussed the concept of the \u0026#39;modern data stack\u0026#39; and what that means.\u003c/p\u003e\u003cp\u003eSpecial Guest: Tristan Zajonc.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Tristan Zajonc (@tristanzajonc) / Twitter\" rel=\"nofollow\" href=\"https://twitter.com/tristanzajonc\"\u003eTristan Zajonc (@tristanzajonc) / Twitter\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Tristan Zajonc | LinkedIn\" rel=\"nofollow\" href=\"https://www.linkedin.com/in/tristanzajonc/\"\u003eTristan Zajonc | LinkedIn\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Continual | AI/ML for Your Cloud Data Warehouse\" rel=\"nofollow\" href=\"https://continual.ai/\"\u003eContinual | AI/ML for Your Cloud Data Warehouse\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Company | Continual - Operational AI for the Enterprise\" rel=\"nofollow\" href=\"https://continual.ai/company\"\u003eCompany | Continual - Operational AI for the Enterprise\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"The Modern Data Stack Ecosystem - Fall 2021 Edition\" rel=\"nofollow\" href=\"https://continual.ai/post/the-modern-data-stack-ecosystem-fall-2021-edition\"\u003eThe Modern Data Stack Ecosystem - Fall 2021 Edition\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"The Future of the Modern Data Stack\" rel=\"nofollow\" href=\"https://continual.ai/post/the-future-of-the-modern-data-stack\"\u003eThe Future of the Modern Data Stack\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Introducing Continual – the missing AI layer for the modern data stack\" rel=\"nofollow\" href=\"https://continual.ai/post/introducing-continual\"\u003eIntroducing Continual – the missing AI layer for the modern data stack\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Cloudera | The Hybrid Data Cloud Company\" rel=\"nofollow\" href=\"https://www.cloudera.com/\"\u003eCloudera | The Hybrid Data Cloud Company\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Tristan Zajonc, Sense Platform // Data Driven #28 // June 2014 (Hosted by FirstMark Capital) - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=_AhX30ZAC2w\"\u003eTristan Zajonc, Sense Platform // Data Driven #28 // June 2014 (Hosted by FirstMark Capital) - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Sense Preview - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=CO2EKtuJpWQ\"\u003eSense Preview - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"DC_THURS on Operational AI for the Modern Data Stack w/ Tristan Zajonc (Continual) - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=Ch23SynrZVg\"\u003eDC_THURS on Operational AI for the Modern Data Stack w/ Tristan Zajonc (Continual) - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Enterprise Machine Learning on K8s: Lessons Learned and the Road... - Timothy Chen \u0026amp; Tristan Zajonc - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=ZFn1OXG-4tM\"\u003eEnterprise Machine Learning on K8s: Lessons Learned and the Road... - Timothy Chen \u0026amp; Tristan Zajonc - YouTube\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"This week I spoke with Tristan Zajonc, the CEO and cofounder of Continual, a company that provides an AI layer for enterprise companies or, as we'll get into in the podcast, the so-called 'modern data stack'.","date_published":"2022-02-10T11:45:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/514366af-956a-4991-aa40-90be4f4c153a.mp3","mime_type":"audio/mpeg","size_in_bytes":42531626,"duration_in_seconds":3544}]},{"id":"0ae22d06-611d-43dd-9f21-38306f444128","title":"Neurosymbolic AI with Mohan Mahadevan","url":"https://podcast.zenml.io/mohan-mahadevan-neurosymbolic-ai","content_text":"Our guest this week was Mohan Mahadevan, a senior VP at Onfido, a machine-learning powered identity verification platform. He has previously worked at Amazon heading up a computer vision team working on robotics applications as well as for many years at KLA, a leading semiconductor hardware company. He holds a doctorate in theoretical physics from Colorado State University.\n\nMohan had mentioned that he thought it might be interesting to discuss neurosymbolic AI, and the implications of a shift towards that as a core paradigm for production AI systems. In particular, we discuss the practical consequences of such a shift, both in terms of team composition as well as infrastructure requirements.Special Guest: Mohan Mahadevan.Links:Mohan Mahadevan - Senior VP, Applied Science - Onfido | LinkedInOnfido | Document ID \u0026amp; Facial Biometrics VerificationNeuro-symbolic AI | IBM Research TeamsAI’s next big leapNeurosymbolic AI - David Cox slidesMIT 6.S191 (2020): Neurosymbolic AI - YouTubeNeurosymbolic AI Explained - YouTube","content_html":"\u003cp\u003eOur guest this week was Mohan Mahadevan, a senior VP at Onfido, a machine-learning powered identity verification platform. He has previously worked at Amazon heading up a computer vision team working on robotics applications as well as for many years at KLA, a leading semiconductor hardware company. He holds a doctorate in theoretical physics from Colorado State University.\u003c/p\u003e\n\n\u003cp\u003eMohan had mentioned that he thought it might be interesting to discuss neurosymbolic AI, and the implications of a shift towards that as a core paradigm for production AI systems. In particular, we discuss the practical consequences of such a shift, both in terms of team composition as well as infrastructure requirements.\u003c/p\u003e\u003cp\u003eSpecial Guest: Mohan Mahadevan.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Mohan Mahadevan - Senior VP, Applied Science - Onfido | LinkedIn\" rel=\"nofollow\" href=\"https://uk.linkedin.com/in/mohan-mahadevan-4999464\"\u003eMohan Mahadevan - Senior VP, Applied Science - Onfido | LinkedIn\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Onfido | Document ID \u0026amp; Facial Biometrics Verification\" rel=\"nofollow\" href=\"https://onfido.com/\"\u003eOnfido | Document ID \u0026amp; Facial Biometrics Verification\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Neuro-symbolic AI | IBM Research Teams\" rel=\"nofollow\" href=\"https://research.ibm.com/teams/neuro-symbolic-ai\"\u003eNeuro-symbolic AI | IBM Research Teams\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"AI’s next big leap\" rel=\"nofollow\" href=\"https://knowablemagazine.org/article/technology/2020/what-is-neurosymbolic-ai\"\u003eAI’s next big leap\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Neurosymbolic AI - David Cox slides\" rel=\"nofollow\" href=\"http://introtodeeplearning.com/2020/slides/6S191_MIT_DeepLearning_L7.pdf\"\u003eNeurosymbolic AI - David Cox slides\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"MIT 6.S191 (2020): Neurosymbolic AI - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=4PuuziOgSU4\"\u003eMIT 6.S191 (2020): Neurosymbolic AI - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Neurosymbolic AI Explained - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=HhymId8dr5Q\"\u003eNeurosymbolic AI Explained - YouTube\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"Our guest this week was Mohan Mahadevan, a senior VP at Onfido, a machine-learning powered identity verification platform. He has previously worked at Amazon heading up a computer vision team working on robotics applications as well as for many years at KLA, a leading semiconductor hardware company. He holds a doctorate in theoretical physics from Colorado State University.","date_published":"2022-01-27T15:00:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/0ae22d06-611d-43dd-9f21-38306f444128.mp3","mime_type":"audio/mpeg","size_in_bytes":42432201,"duration_in_seconds":3535}]},{"id":"137ca303-bc89-4424-ad78-37be0158a842","title":"Creating Tools that Spark Joy with Ines Montani","url":"https://podcast.zenml.io/ines-montani","content_text":"Our guest this week is Ines Montani, co-founder and CEO of Explosion, a company based out of Berlin that produce tools that you probably know and love like Spacy, a Python Natural Language Processing library and Prodigy, a data annotation tool.\n\nI've always found Ines to be personally inspiring in the work that she and her team produce as well as how they present themselves to the world, so it was a real pleasure to get to dive into the weeds as to exactly how that happens. We also discuss how NLP works in production, what reproducibility means for ML projects and much more.Special Guest: Ines Montani.Links:ines.ioExplosion · Makers of spaCy, Prodigy, and other AI and NLP developer toolsSoftware · ExplosionspaCy · Industrial-strength Natural Language Processing in Pythonexplosion/spaCy: 💫 Industrial-strength Natural Language Processing (NLP) in PythonProdigy · An annotation tool for AI, Machine Learning \u0026amp; NLPLive Demo · Prodigy · An annotation tool for AI, Machine Learning \u0026amp; NLPThinc · A refreshing functional take on deep learningexplosion/thinc: 🔮 A refreshing functional take on deep learning, compatible with your favorite librariesines/spacy-course: 👩‍🏫 Advanced NLP with spaCy: A free online course\"Let Them Write Code\" - Keynote - Ines Montani - YouTube","content_html":"\u003cp\u003eOur guest this week is Ines Montani, co-founder and CEO of Explosion, a company based out of Berlin that produce tools that you probably know and love like Spacy, a Python Natural Language Processing library and Prodigy, a data annotation tool.\u003c/p\u003e\n\n\u003cp\u003eI\u0026#39;ve always found Ines to be personally inspiring in the work that she and her team produce as well as how they present themselves to the world, so it was a real pleasure to get to dive into the weeds as to exactly how that happens. We also discuss how NLP works in production, what reproducibility means for ML projects and much more.\u003c/p\u003e\u003cp\u003eSpecial Guest: Ines Montani.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"ines.io\" rel=\"nofollow\" href=\"https://ines.io/\"\u003eines.io\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Explosion · Makers of spaCy, Prodigy, and other AI and NLP developer tools\" rel=\"nofollow\" href=\"https://explosion.ai/\"\u003eExplosion · Makers of spaCy, Prodigy, and other AI and NLP developer tools\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Software · Explosion\" rel=\"nofollow\" href=\"https://explosion.ai/software#spacy\"\u003eSoftware · Explosion\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"spaCy · Industrial-strength Natural Language Processing in Python\" rel=\"nofollow\" href=\"https://spacy.io/\"\u003espaCy · Industrial-strength Natural Language Processing in Python\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"explosion/spaCy: 💫 Industrial-strength Natural Language Processing (NLP) in Python\" rel=\"nofollow\" href=\"https://github.com/explosion/spaCy\"\u003eexplosion/spaCy: 💫 Industrial-strength Natural Language Processing (NLP) in Python\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Prodigy · An annotation tool for AI, Machine Learning \u0026amp; NLP\" rel=\"nofollow\" href=\"https://prodi.gy/\"\u003eProdigy · An annotation tool for AI, Machine Learning \u0026amp; NLP\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Live Demo · Prodigy · An annotation tool for AI, Machine Learning \u0026amp; NLP\" rel=\"nofollow\" href=\"https://prodi.gy/demo\"\u003eLive Demo · Prodigy · An annotation tool for AI, Machine Learning \u0026amp; NLP\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Thinc · A refreshing functional take on deep learning\" rel=\"nofollow\" href=\"https://thinc.ai/\"\u003eThinc · A refreshing functional take on deep learning\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"explosion/thinc: 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries\" rel=\"nofollow\" href=\"https://github.com/explosion/thinc\"\u003eexplosion/thinc: 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"ines/spacy-course: 👩‍🏫 Advanced NLP with spaCy: A free online course\" rel=\"nofollow\" href=\"https://github.com/ines/spacy-course\"\u003eines/spacy-course: 👩‍🏫 Advanced NLP with spaCy: A free online course\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"\u0026quot;Let Them Write Code\u0026quot; - Keynote - Ines Montani - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=Ivb4AAuj5JY\"\u003e\u0026quot;Let Them Write Code\u0026quot; - Keynote - Ines Montani - YouTube\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"Our guest this week is Ines Montani, co-founder and CEO of Explosion, a company based out of Berlin that produce tools that you probably know and love like Spacy, a Python Natural Language Processing library and Prodigy, a data annotation tool.","date_published":"2022-01-13T17:00:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/137ca303-bc89-4424-ad78-37be0158a842.mp3","mime_type":"audio/mpeg","size_in_bytes":31518144,"duration_in_seconds":2626}]},{"id":"d5f677ea-92d0-421a-85e2-918f549ec265","title":"Monitoring Your Way to ML Production Nirvana with Danny Leybzon","url":"https://podcast.zenml.io/ml-monitoring-danny-leybzon","content_text":"This week, we spoke with Danny Leybzon, currently working with WhyLabs to help data scientists monitor their models in production and prevent model performance from degrading. He previously worked as a kind of roving data scientist and engineer, helping companies put their models into production.\n\nAs such, we had a really interesting discussion of some of the ways that tooling and the general context for data science sometimes lets practitioners down, \nAnd of course we also discussed why monitoring and logging is actually a kind of baseline practice that should be part of any and every data scientist's toolkit. Luckily for us, Danny added in a bunch of examples from his wide experience doing all this in the real world.Special Guest: Danny Leybzon.Links:Danny D. Leybzonwhylogs · PyPIData and AI Observability Platform - enabling MLOps | WhyLabsSLCPython December 2020: Monitoring Machine Learning with Danny Leybzon - YouTubeMonitoring ML Models - YouTubeMonitoring ML Models in Production - YouTubeMachine Learning Models in Production - YouTubeDanny on LinkedInWomen's Clothes | Men's Clothes | Kid's Clothing Boxes | Stitch Fixzenml-io/zenml: ZenML 🙏: MLOps framework to create reproducible ML pipelines for production machine learning.Terraform by HashiCorpZillow — A Cautionary Tale of Machine Learning - causaLensCloud Monitoring as a Service | DatadogPrometheus - Monitoring system \u0026amp; time series database","content_html":"\u003cp\u003eThis week, we spoke with Danny Leybzon, currently working with WhyLabs to help data scientists monitor their models in production and prevent model performance from degrading. He previously worked as a kind of roving data scientist and engineer, helping companies put their models into production.\u003c/p\u003e\n\n\u003cp\u003eAs such, we had a really interesting discussion of some of the ways that tooling and the general context for data science sometimes lets practitioners down, \u003cbr\u003e\nAnd of course we also discussed why monitoring and logging is actually a kind of baseline practice that should be part of any and every data scientist\u0026#39;s toolkit. Luckily for us, Danny added in a bunch of examples from his wide experience doing all this in the real world.\u003c/p\u003e\u003cp\u003eSpecial Guest: Danny Leybzon.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Danny D. Leybzon\" rel=\"nofollow\" href=\"http://web.dleybz.co/\"\u003eDanny D. Leybzon\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"whylogs · PyPI\" rel=\"nofollow\" href=\"https://pypi.org/project/whylogs/\"\u003ewhylogs · PyPI\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Data and AI Observability Platform - enabling MLOps | WhyLabs\" rel=\"nofollow\" href=\"https://whylabs.ai/\"\u003eData and AI Observability Platform - enabling MLOps | WhyLabs\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"SLCPython December 2020: Monitoring Machine Learning with Danny Leybzon - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=kTJASJbhpGs\"\u003eSLCPython December 2020: Monitoring Machine Learning with Danny Leybzon - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Monitoring ML Models - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=Jn-RNwrP5O0\"\u003eMonitoring ML Models - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Monitoring ML Models in Production - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=oUcuilWWX78\"\u003eMonitoring ML Models in Production - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Machine Learning Models in Production - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/watch?v=3lSVZi2Dcjg\"\u003eMachine Learning Models in Production - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Danny on LinkedIn\" rel=\"nofollow\" href=\"https://www.linkedin.com/in/dleybz/\"\u003eDanny on LinkedIn\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Women\u0026#39;s Clothes | Men\u0026#39;s Clothes | Kid\u0026#39;s Clothing Boxes | Stitch Fix\" rel=\"nofollow\" href=\"https://www.stitchfix.com/\"\u003eWomen\u0026#39;s Clothes | Men\u0026#39;s Clothes | Kid\u0026#39;s Clothing Boxes | Stitch Fix\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"zenml-io/zenml: ZenML 🙏: MLOps framework to create reproducible ML pipelines for production machine learning.\" rel=\"nofollow\" href=\"https://github.com/zenml-io/zenml\"\u003ezenml-io/zenml: ZenML 🙏: MLOps framework to create reproducible ML pipelines for production machine learning.\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Terraform by HashiCorp\" rel=\"nofollow\" href=\"https://www.terraform.io/\"\u003eTerraform by HashiCorp\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Zillow — A Cautionary Tale of Machine Learning - causaLens\" rel=\"nofollow\" href=\"https://www.causalens.com/blog/zillow-a-cautionary-tale-of-machine-learning/\"\u003eZillow — A Cautionary Tale of Machine Learning - causaLens\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Cloud Monitoring as a Service | Datadog\" rel=\"nofollow\" href=\"https://www.datadoghq.com/\"\u003eCloud Monitoring as a Service | Datadog\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Prometheus - Monitoring system \u0026amp; time series database\" rel=\"nofollow\" href=\"https://prometheus.io/\"\u003ePrometheus - Monitoring system \u0026amp; time series database\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"This week, we spoke with Danny Leybzon, currently working with WhyLabs to help data scientists monitor their models in production and prevent model performance from degrading. He previously worked as a kind of roving data scientist and engineer, helping companies put their models into production.","date_published":"2021-12-16T16:30:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/d5f677ea-92d0-421a-85e2-918f549ec265.mp3","mime_type":"audio/mpeg","size_in_bytes":38951010,"duration_in_seconds":2434}]},{"id":"ca401b71-399e-4ce5-9497-f155b2be108b","title":"Practical MLOps with Noah Gift","url":"https://podcast.zenml.io/practical-mlops-noah-gift","content_text":"Noah Gift is the founder of Pragmatic A.I. Labs and author of 'Practical MLOps'. We discuss the role of MLOps in an organisation, some deployment war stories from his career as well as what he considers to be 'best practices' in production machine learning.\n\nRead the summary blogpost on the ZenML blog.Special Guest: Noah Gift.Links:Noah GiftPragmatic AI Labs | Pragmatic AI Labs and SolutionsPractical MLOps [Book]Pragmatic AI Labs - YouTubenoahgift (Noah Gift)The Black Swan: Second Edition: The Impact of the Highly Improbable: With a new section: \"On Robustness and Fragility\" (Incerto): Taleb, Nassim Nicholas: 8601404990557: Amazon.com: BooksWhy the iBuying algorithms failed Zillow, and what it says about the business world’s love affair with AI - GeekWireJenkinsAmazon.com: Leonardo da Vinci eBook : Isaacson, Walter: Kindle StoreHelicopter by Leonardo da VinciAmazon SageMaker – Machine Learning – Amazon Web ServicesBeware the data science pin factory: The power of the full-stack data science generalist and the perils of division of labor through function | Stitch Fix Technology – MultithreadedAmazon Mechanical TurkDevOps - WikipediaWhat is AutoML? | IBMKubernetesOpen Source MLOps Orchestration | MLRunAmazon EFSAmazon FSx | Feature-Rich \u0026amp; Highly-Performant File Systems | Amazon Web ServicesCloud Computing Services | Microsoft AzureAzure ML StudioServerless Computing - AWS Lambda - Amazon Web ServicesGoogle Cloud Platform - Getting Started","content_html":"\u003cp\u003eNoah Gift is the founder of Pragmatic A.I. Labs and author of \u0026#39;Practical MLOps\u0026#39;. We discuss the role of MLOps in an organisation, some deployment war stories from his career as well as what he considers to be \u0026#39;best practices\u0026#39; in production machine learning.\u003c/p\u003e\n\n\u003cp\u003eRead \u003ca href=\"https://blog.zenml.io/practical-mlops-noah-gift/\" rel=\"nofollow\"\u003ethe summary blogpost\u003c/a\u003e on the ZenML blog.\u003c/p\u003e\u003cp\u003eSpecial Guest: Noah Gift.\u003c/p\u003e\u003cp\u003eLinks:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003ca title=\"Noah Gift\" rel=\"nofollow\" href=\"https://noahgift.com/\"\u003eNoah Gift\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Pragmatic AI Labs | Pragmatic AI Labs and Solutions\" rel=\"nofollow\" href=\"https://paiml.com/\"\u003ePragmatic AI Labs | Pragmatic AI Labs and Solutions\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Practical MLOps [Book]\" rel=\"nofollow\" href=\"https://www.oreilly.com/library/view/practical-mlops/9781098103002/\"\u003ePractical MLOps [Book]\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Pragmatic AI Labs - YouTube\" rel=\"nofollow\" href=\"https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q\"\u003ePragmatic AI Labs - YouTube\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"noahgift (Noah Gift)\" rel=\"nofollow\" href=\"https://github.com/noahgift\"\u003enoahgift (Noah Gift)\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"The Black Swan: Second Edition: The Impact of the Highly Improbable: With a new section: \u0026quot;On Robustness and Fragility\u0026quot; (Incerto): Taleb, Nassim Nicholas: 8601404990557: Amazon.com: Books\" rel=\"nofollow\" href=\"https://www.amazon.com/Black-Swan-Improbable-Robustness-Fragility/dp/081297381X/ref=sr_1_2?sr=8-2\u0026amp;keywords=black%2Bswan\u0026amp;tag=soumet-20\u0026amp;qid=1638281965\"\u003eThe Black Swan: Second Edition: The Impact of the Highly Improbable: With a new section: \u0026quot;On Robustness and Fragility\u0026quot; (Incerto): Taleb, Nassim Nicholas: 8601404990557: Amazon.com: Books\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Why the iBuying algorithms failed Zillow, and what it says about the business world’s love affair with AI - GeekWire\" rel=\"nofollow\" href=\"https://www.geekwire.com/2021/ibuying-algorithms-failed-zillow-says-business-worlds-love-affair-ai/\"\u003eWhy the iBuying algorithms failed Zillow, and what it says about the business world’s love affair with AI - GeekWire\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Jenkins\" rel=\"nofollow\" href=\"https://www.jenkins.io/\"\u003eJenkins\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Amazon.com: Leonardo da Vinci eBook : Isaacson, Walter: Kindle Store\" rel=\"nofollow\" href=\"https://www.amazon.com/Leonardo-Vinci-Walter-Isaacson-ebook/dp/B071Y385Q1/ref=sr_1_1?sr=8-1\u0026amp;keywords=da%2Bvinci%2Bwalter\u0026amp;tag=soumet-20\u0026amp;qid=1638282087\"\u003eAmazon.com: Leonardo da Vinci eBook : Isaacson, Walter: Kindle Store\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Helicopter by Leonardo da Vinci\" rel=\"nofollow\" href=\"https://www.leonardo-da-vinci.net/helicopter/\"\u003eHelicopter by Leonardo da Vinci\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Amazon SageMaker – Machine Learning – Amazon Web Services\" rel=\"nofollow\" href=\"https://aws.amazon.com/sagemaker/\"\u003eAmazon SageMaker – Machine Learning – Amazon Web Services\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Beware the data science pin factory: The power of the full-stack data science generalist and the perils of division of labor through function | Stitch Fix Technology – Multithreaded\" rel=\"nofollow\" href=\"https://multithreaded.stitchfix.com/blog/2019/03/11/FullStackDS-Generalists/\"\u003eBeware the data science pin factory: The power of the full-stack data science generalist and the perils of division of labor through function | Stitch Fix Technology – Multithreaded\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Amazon Mechanical Turk\" rel=\"nofollow\" href=\"https://www.mturk.com/\"\u003eAmazon Mechanical Turk\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"DevOps - Wikipedia\" rel=\"nofollow\" href=\"https://en.wikipedia.org/wiki/DevOps\"\u003eDevOps - Wikipedia\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"What is AutoML? | IBM\" rel=\"nofollow\" href=\"https://www.ibm.com/cloud/learn/automl\"\u003eWhat is AutoML? | IBM\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Kubernetes\" rel=\"nofollow\" href=\"https://kubernetes.io/\"\u003eKubernetes\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Open Source MLOps Orchestration | MLRun\" rel=\"nofollow\" href=\"https://www.mlrun.org/\"\u003eOpen Source MLOps Orchestration | MLRun\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Amazon EFS\" rel=\"nofollow\" href=\"https://aws.amazon.com/efs/\"\u003eAmazon EFS\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Amazon FSx | Feature-Rich \u0026amp; Highly-Performant File Systems | Amazon Web Services\" rel=\"nofollow\" href=\"https://aws.amazon.com/fsx/\"\u003eAmazon FSx | Feature-Rich \u0026amp; Highly-Performant File Systems | Amazon Web Services\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Cloud Computing Services | Microsoft Azure\" rel=\"nofollow\" href=\"https://azure.microsoft.com/en-us/\"\u003eCloud Computing Services | Microsoft Azure\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Azure ML Studio\" rel=\"nofollow\" href=\"https://ml.azure.com/\"\u003eAzure ML Studio\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Serverless Computing - AWS Lambda - Amazon Web Services\" rel=\"nofollow\" href=\"https://aws.amazon.com/lambda/\"\u003eServerless Computing - AWS Lambda - Amazon Web Services\u003c/a\u003e\u003c/li\u003e\u003cli\u003e\u003ca title=\"Google Cloud Platform - Getting Started\" rel=\"nofollow\" href=\"https://console.cloud.google.com/getting-started\"\u003eGoogle Cloud Platform - Getting Started\u003c/a\u003e\u003c/li\u003e\u003c/ul\u003e","summary":"Noah Gift is the founder of Pragmatic A.I. Labs and author of 'Practical MLOps'. We discuss the role of MLOps in an organisation, some deployment war stories from his career as well as what he considers to be 'best practices' in production machine learning.","date_published":"2021-12-02T09:45:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/ca401b71-399e-4ce5-9497-f155b2be108b.mp3","mime_type":"audio/mpeg","size_in_bytes":46105047,"duration_in_seconds":2834}]},{"id":"094212f9-84ef-4cb3-8c2a-87230f3cef0a","title":"Introducing ZenML","url":"https://podcast.zenml.io/introducing-zenml","content_text":"Adam and Hamza introduce themselves for the first episode of Pipeline Conversations. They discuss the world of MLOps, where ZenML sits within this space, and why it's such a complicated problem to solve.","content_html":"\u003cp\u003eAdam and Hamza introduce themselves for the first episode of Pipeline Conversations. They discuss the world of MLOps, where ZenML sits within this space, and why it\u0026#39;s such a complicated problem to solve.\u003c/p\u003e","summary":"Adam and Hamza introduce themselves for the first episode of Pipeline Conversations. They discuss the world of MLOps, where ZenML sits within this space, and why it's such a complicated problem to solve.","date_published":"2021-11-19T12:00:00.000+01:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/4d525632-f8ef-47c1-9321-20f5c498b1ac/094212f9-84ef-4cb3-8c2a-87230f3cef0a.mp3","mime_type":"audio/mpeg","size_in_bytes":21411204,"duration_in_seconds":1338}]}]}