web02.fireside.fm Sat, 25 Apr 2026 11:25:54 -0500 Fireside (https://fireside.fm) Pipeline Conversations - Episodes Tagged with “Tools” https://podcast.zenml.io/tags/tools Mon, 05 Sep 2022 09:00:00 +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 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:

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