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Bringing DevOps Best Practices into Machine Learning with Benedikt Koller from ZenML

Podcast |: Machine Learning Engineered
Publisher |: Charlie You
Media Type |: audio
Podknife tags |: Artificial Intelligence,; Business,; Careers,; Data Science,; Interview,; Machine Learning,; Technology
Categories Via RSS |: Business,; Careers,; Science,; Technology
Publication Date |: Mar 02, 2021
Episode Duration |: 01:28:19

Description
iTunes Summary

Benedikt Koller is a self-professed "Ops guy", having spent over 12 years working in roles such as DevOps engineer, platform engineer, and infrastructure tech lead at companies like Stylight and Talentry in addition to his own consultancy KEMB. He's recently dove head first into the world of ML, where he hopes to bring his extensive ops knowledge into the field as the co-founder of Maiot, the company behind ZenML, an open source MLOps framework.

Learn more:

https://zenml.io/

https://maiot.io/

Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: https://www.cyou.ai/newsletter

Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

Subscribe to ML Engineered: https://mlengineered.com/listen

Comments? Questions? Submit them here: http://bit.ly/mle-survey

Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Timestamps:

02:15 Introducing Benedikt Koller

05:30 What the "DevOps revolution" was

10:10 Bringing good Ops practices into ML projects

30:50 Pivoting from vehicle predictive analytics to open source ML tooling

34:35 Design decisions made in ZenML

39:20 Most common problems faced by applied ML teams

49:00 The importance of separating configurations from code

55:25 Resources Ben recommends for learning Ops

57:30 What to monitor in an ML pipelines

01:00:45 Why you should run experiments in automated pipelines

01:08:20 The essential components of an MLOps stack

01:10:25 Building an open source business and what's next for ZenML

01:20:20 Rapid fire questions

Links:

ZenML's GitHub

Maiot Blog

The Twelve Factor App

12 Factors of reproducible Machine Learning in production

Seldon

Pachyderm

KubeFlow

Something Deeply Hidden

The Expanse Series

The Three Body Problem

Extreme Ownership

Benedikt discusses common problems faced by teams putting machine learning into production, bringing over best practices from DevOps to solve them, and building ZenML, an open source MLOps framework.