web02.fireside.fm Sat, 25 Apr 2026 11:23:15 -0500 Fireside (https://fireside.fm) Pipeline Conversations - Episodes Tagged with “Infrastructure” https://podcast.zenml.io/tags/infrastructure Thu, 27 Oct 2022 07:00:00 +0200 Pipeline Conversations brings you interviews with platform engineers, ML practitioners, and technical leaders building production AI systems. We dig into the real challenges of MLOps and LLMOps: orchestrating complex workflows on Kubernetes, fine-tuning and evaluating models at scale, and shipping AI that actually works. From ZenML. en-us episodic MLOps and LLMOps, from the trenches ZenML GmbH Pipeline Conversations brings you interviews with platform engineers, ML practitioners, and technical leaders building production AI systems. We dig into the real challenges of MLOps and LLMOps: orchestrating complex workflows on Kubernetes, fine-tuning and evaluating models at scale, and shipping AI that actually works. From ZenML. no machine-learning, machinelearning, mlops, deeplearning, ai, artificialintelligence, artificial-intelligence, technology, tech, mlops, llmops ZenML GmbH podcast@zenml.io Questioning MLOps with Lak Lakshmanan https://podcast.zenml.io/lak-lakshmanan 253cd080-cfca-4b29-9a53-1641ec9b384b Thu, 27 Oct 2022 07:00:00 +0200 ZenML GmbH full 2 ZenML GmbH 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. 53:02 no 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. Lak 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. mlops, machine-learning, data-science, ai, artificial-intelligence, infrastructure, scale 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.

Lak 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:

]]> 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.

Lak 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:

]]> Educating the next generation with Goku Mohandas https://podcast.zenml.io/goku-mohandas-made-with-ml 11960a13-d427-400c-8105-382ac76bc27f Thu, 29 Sep 2022 09:00:00 +0200 ZenML GmbH full 2 ZenML GmbH 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. 1:08:43 no In today's conversation, I'm speaking with Goku Mohandas, founder and creator of the amazing online resource MadeWithML (https://madewithml.com/). Goku has a bunch of practical experience, from working with Apple to a startup in the oncology space and much more. In 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 (https://madewithml.com/). We 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. Goku 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 (https://zenml.io/competition). Special Guest: Goku Mohandas. mlops, machine-learning, data-science, ai, artificial-intelligence, infrastructure, education, medicine 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.

In 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.

We 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.

Goku 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:

]]> 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.

In 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.

We 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.

Goku 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:

]]> ML Abstractions with Phil Howes https://podcast.zenml.io/ml-abstractions-phil-howes 38e30182-2cf3-4295-9294-629edca09548 Mon, 05 Sep 2022 09:00:00 +0200 ZenML GmbH full 2 ZenML GmbH 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. 54:13 no 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. We 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. Phil 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. mlops, machine-learning, data-science, ai, infrastructure, pipelines, tools, platforms 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.

We 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.

Phil 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:

]]> 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.

We 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.

Phil 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:

]]> Building MLOps Tools with Outerbounds https://podcast.zenml.io/building-mlops-tools-outerbounds-metaflow b5a7fa67-7d9f-46af-a184-bfac8759bae4 Mon, 22 Aug 2022 08:00:00 +0200 ZenML GmbH full 2 ZenML GmbH 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. 59:43 no 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. Above 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. mlops, machine-learning, data-science, ai, infrastructure, pipelines, tools 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.

Above 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:

]]> 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.

Above 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:

]]> ML Engineering with Ben Wilson https://podcast.zenml.io/ml-engineering-ben-wilson 7d3547bd-c623-47fb-ad0b-c59658a40296 Wed, 08 Jun 2022 16:00:00 +0200 ZenML GmbH full 2 ZenML GmbH 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'. 1:04:41 no 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. 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 (https://www.manning.com/books/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. I 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. mlops, machine-learning, data-science, ai, infrastructure, pipelines, tools 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.

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'. 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.

I 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:

]]> 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.

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'. 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.

I 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:

]]> Open-Source MLOps with Matt Squire https://podcast.zenml.io/open-source-mlops-matt-squire 3d58b3bb-2933-41cd-a32f-0c59343c894a Thu, 31 Mar 2022 11:00:00 +0200 ZenML GmbH full ZenML GmbH 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. 47:41 no This week I spoke with Matt Squire, the CTO and co-founder of Fuzzy Labs (https://www.fuzzylabs.ai), where they help partner organisations think through how best to productionise their machine learning workflows. Matt and FuzzyLabs are also behind the Awesome Open Source MLOps (https://github.com/fuzzylabs/awesome-open-mlops) GitHub repo where you can find all the options for an open-source MLOps stack of your dreams. Matt 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. mlops, machine-learning, data-science, ai, artificial-intelligence, infrastructure, open-source 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.

Matt 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.

Matt 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:

]]> 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.

Matt 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.

Matt 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:

]]> Practical Production ML with Emmanuel Ameisen https://podcast.zenml.io/ml-powered-emmanuel-ameisen 4b687c82-9d24-4df0-9831-06c8704924c1 Thu, 17 Mar 2022 17:00:00 +0100 ZenML GmbH full ZenML GmbH 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. 58:00 no 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. Emmanuel 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. mlops, machine-learning, data-science, ai, artificial-intelligence, infrastructure 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.

Emmanuel 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:

]]> 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.

Emmanuel 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:

]]> The Modern Data Stack with Tristan Zajonc https://podcast.zenml.io/modern-data-stack-tristan-zajonc 514366af-956a-4991-aa40-90be4f4c153a Thu, 10 Feb 2022 11:45:00 +0100 ZenML GmbH full ZenML GmbH 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'. 59:04 no This week I spoke with Tristan Zajonc (https://www.linkedin.com/in/tristanzajonc/), the CEO and cofounder of Continual (https://continual.ai/), 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'. He 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. In 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. mlops, machine-learning, data-science, ai, artificial-intelligence, infrastructure 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'.

He 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.

In 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:

]]> 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'.

He 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.

In 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:

]]> Neurosymbolic AI with Mohan Mahadevan https://podcast.zenml.io/mohan-mahadevan-neurosymbolic-ai 0ae22d06-611d-43dd-9f21-38306f444128 Thu, 27 Jan 2022 15:00:00 +0100 ZenML GmbH full ZenML GmbH 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. 58:55 no 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. Mohan 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. mlops, machine-learning, data-science, ai, artificial-intelligence, infrastructure 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.

Mohan 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:

]]> 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.

Mohan 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:

]]>