6min chapter

MLOps.community  cover image

Building an ML Platform: Insights, Community, and Advocacy // Stephen Batifol // #178

MLOps.community

CHAPTER

MLflow, Qflow, and the Challenges of MLOps

The speakers discuss the popularity, drawbacks, and frustrations with MLflow and Qflow in the MLOps community. They also highlight the difficulties faced by data scientists in using Qflow and the suggestion to learn Kubernetes, leading to data scientists becoming SREs. The chapter also explores the decision to use flights for ML workflows and the benefits of using the open-source platform Call for deployment.

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