web02.fireside.fm Sat, 18 Apr 2026 05:09:14 -0500 Fireside (https://fireside.fm) Pipeline Conversations - Episodes Tagged with “Ethics” https://podcast.zenml.io/tags/ethics Thu, 14 Apr 2022 11:00:00 +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 Trustworthy ML with Kush Varshney https://podcast.zenml.io/trustworthy-ml-kush-varshney 3b306917-5653-40d1-b3c7-85c92ac80ad3 Thu, 14 Apr 2022 11:00:00 +0200 ZenML GmbH full ZenML GmbH 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. 39:08 no I enthusiastically read Kush Varshney's book when it was released for free to the world several months back. Trustworthy Machine Learning (http://www.trustworthymachinelearning.com/) 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 (http://krvarshney.github.io/) on to talk more about his work and research. I 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 (https://ai-fairness-360.org/) and AI Explainability 360 (https://ai-explainability-360.org/) toolkits that I'm sure listeners of this podcast would find worth checking out. Special Guest: Kush Varshney. machine-learning, data-science, ai, artificial-intelligence, ethics, fairness, bias 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.

I 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:

]]> 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.

I 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:

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