MLOps.community
Building an ML Platform: Insights, Community, and Advocacy // Stephen Batifol // #178
Oct 3, 2023
Stephen Batifol, data scientist at Wolt, shares insights on building an ML platform, developer relations, and creating a thriving internal community. They discuss the challenges of onboarding data scientists, importance of documentation, simplifying the developer experience, and expanding services. They also touch upon MLflow, Qflow, observability, training models with multiple countries, building trust through feedback, and attracting talent through talks and content sharing.
45:48
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Quick takeaways
- Building an internal ML platform at Vault prioritizes the needs of data scientists and fosters a community of knowledge sharing.
- Meetups play a significant role in fostering the tech community in Berlin and attracting tech talent to the city.
Deep dives
Creating an ML Platform at Vault
One of the key insights from the podcast episode is the discussion around creating an internal ML platform at Vault. The guest, a former ML platform engineer turned internal developer advocate, talks about the importance of advocating and supporting the use of the platform by teams and data scientists. The platform is designed to make the data scientists' job easier by providing a range of features, such as dynamic workflows, resource allocation, and easy deployment options. The guest also emphasizes the significance of community building within the platform, fostering knowledge sharing and supporting data scientists in their work.
