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

MLOps Coffee Sessions #178 with Stephen Batifol, Building an ML Platform: Insights, Community, and Advocacy.

// Abstract

Discover how Wolt onboards data scientists onto the platform and builds a thriving internal community of users. Stephen's firsthand experiences shed light on the importance of developer relations and how they contribute to making data scientists' lives easier. From top-notch documentation to getting-started guides and tutorials, the internal platform at Wolt prioritizes the needs of its users.

// Bio

From Android developer to Data Scientist to Machine Learning Engineer, Stephen has a wealth of software engineering experience at Wolt. He believes that machine learning has a lot to learn from software engineering best practices and spends his time making ML deployments simple for other engineers. Stephen is also a founding member and organizer of the MLOps.community Meetups in Berlin.

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// Related Links

⁠--------------- ✌️Connect With Us ✌️ -------------

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Stephen on LinkedIn: https://www.linkedin.com/in/stephen-batifol/

Timestamps:

[00:00] Stephen's preferred coffee

[00:32] Takeaways

[01:35] Please like, share, and subscribe to our MLOps channels!

[03:00] Creating his own team!

[04:44] DevRel

[06:32] The door dash of Europe

[11:28] Data platform underneath

[12:55] Cellular core deployment uses open source

[14:21] Alibi

[16:08] Kafka

[16:59] Selling points to data scientists

[20:05] Language models concern data scientists

[22:12] Incorporating LLMs into the business

[23:55] Feedback from data scientists and end users

[27:37] User surveys

[30:11] Evangelizing and giving talks

[35:25] Tech Hub Culture in Berlin

[38:38] Kubernetes lifestyle

[42:55] Interacting with SREs

[45:28] Wrap up