Last month, on April 21 and 22, Tecton hosted apply(): the ML data engineering conference to share the latest best practices in ML data engineering. The conference brought together industry thought leaders and practitioners from over 30 leading organizations and over 7,000 registrants from 60+ countries around the world.
Originally, we planned apply() to be a small community meetup for ML data practitioners to share ideas and keep their fingers on the pulse of the latest trends in operational ML (ML used for online, automated decision making). We brought together some of the leading experts in the community like Wes McKinney (Ursa, Pandas, Apache Arrow), Chip Huyen (Stanford), and Todd Underwood (Google). We were also thrilled to host talks by practitioners from companies like DoorDash, Etsy, Netflix, Pinterest, Spotify, and Stitch Fix. Before we knew it, the word spread, and we quickly found ourselves buying more capacity on Zoom to meet the interest of over 7,000 registered attendees from around the world.
We learned a lot at apply(). Of course, talks like “Supercharging our Data Scientists’ Productivity at Netflix” and “Feature Stores at Spotify: Building & Scaling a Centralized Platform” taught us how ML leaders achieve operational ML at scale, but we learned even more from our audience and community. Thoughtful questions and discussions in our Slack as the talks went on made this virtual conference feel like a tight-knit community brainstorming session. As our friends at DoorDash said in their talk, the best way to advance the space of ML data engineering is to share our experiences and what we have learned with each other. We think apply() serves as a great demonstration of that spirit.
As the conference came to a close last week, we received great feedback from the community. Attendees rated the conference as very valuable with an average rating of 4.6 out of 5. This quote summarizes: “You had all the right people, all the right topics, and you stitched it together beautifully. This was such an amazing resource and I can’t thank you enough for making it happen!” While we’re proud to have enabled such valuable information sharing, the community’s reception to the conference is really a testament to the fact that ML data practitioners are looking for opportunities to share the latest and greatest thinking in operationalizing ML. We’re excited to build and maintain that forum with apply() and the Tecton/Feast Slack community.
While the operational ML space is in its early days, apply() made it clear that its importance to the future of software is no secret. After apply(), we’re more energized than ever to keep building and working with ML data practitioners to accelerate the advent of operational ML. It was a blast hosting apply() and we can’t wait to do it again soon. Stay tuned!