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

Machine Learning Engineered

Mar 2 2021 • 1 hr 28 mins

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: ( ( Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: ( Follow Charlie on Twitter: ( Subscribe to ML Engineered: ( Comments? Questions? Submit them here: ( Take the Giving What We Can Pledge: ( 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)