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

Episode 23

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

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.

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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


ZenML's GitHub

Maiot Blog

The Twelve Factor App

12 Factors of reproducible Machine Learning in production




Something Deeply Hidden

The Expanse Series

The Three Body Problem

Extreme Ownership

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Machine Learning Engineered
Helping you bring ML out of the lab and into products that people love.

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Charlie You

Charlie currently works as a Machine Learning Engineer at Workday. He graduated from RPI with a B.S. in Computer Science and taught himself ML through online courses and projects. In his free time, Charlie enjoys investing and trading, playing poker, and training Brazilian Jiu-Jitsu.