Pipeline Conversations
A Machine Learning Podcast by ZenML
Displaying Episode 11 - 20 of 26 in total of Pipeline Conversations with the tag “mlops”.
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ZenML MLOps Competition
September 26th, 2022 | Season 2 | 8 mins 13 secs
competition, mlops, open-source, zenml
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!
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ML Abstractions with Phil Howes
September 5th, 2022 | Season 2 | 54 mins 13 secs
ai, data-science, infrastructure, machine-learning, mlops, pipelines, platforms, tools
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.
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Building MLOps Tools with Outerbounds
August 22nd, 2022 | Season 2 | 59 mins 43 secs
ai, data-science, infrastructure, machine-learning, mlops, 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.
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Safe and Testable Computer Vision with Lakera
August 4th, 2022 | Season 2 | 57 mins 32 secs
computer-vision, data, machine-learning, mlops, monitoring, safety, testing
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.
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Satellite Vision with Robin Cole
July 28th, 2022 | Season 2 | 47 mins 56 secs
computer-vision, data-science, deep-learning, mlops, satellite, serverless
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.
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ML Monitoring with Emeli Dral
July 7th, 2022 | Season 2 | 46 mins 57 secs
data, machine-learning, mlops, monitoring
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.
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ML Engineering with Ben Wilson
June 8th, 2022 | Season 2 | 1 hr 4 mins
ai, data-science, infrastructure, machine-learning, mlops, pipelines, tools
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'.
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ZenML Recap with Adam and Hamza
April 28th, 2022 | 25 mins 31 secs
mlops, open-source, zenml
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.
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Open-Source MLOps with Matt Squire
March 31st, 2022 | 47 mins 41 secs
ai, artificial-intelligence, data-science, infrastructure, machine-learning, mlops, 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.
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Practical Production ML with Emmanuel Ameisen
March 17th, 2022 | 58 mins
ai, artificial-intelligence, data-science, infrastructure, machine-learning, mlops
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.