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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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
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