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 onboard data scientists onto the platform and build 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. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ 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 concerns of 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