Productionize AI ML Faster, and with Less Tech Debt

If your AI/ML projects are moving too slowly, you might be like the 1000s of other ML teams out there who see the biggest culprit to dev velocity being the complex data engineering involved. If building, managing, and maintaining feature pipelines is tieing you and your team down, you’ll want to check out this talk.
In this talk, David Wang and Sergio Ferragut at Tecton will present the data engineering bottleneck for ML. You’ll see how Tecton’s feature platform streamlines feature experimentation and production for ML, accelerating model production by 80% and improving model accuracy.
David will dive into real-world scenarios, such as building a real-time recommender system and explain how Tecton’s declarative framework simplifies the feature development workflow. Sergio will take you through a demo of Tecton’s platform from feature experimentation, productionization, governance, to serving.
What you’ll get out of this talk:
-A complete picture of the data engineering bottleneck in ML and why it matters.
-An introduction to Tecton and how it facilitates a faster and easier development workflow.
-A walk through of Tecton’s feature platform for a real-time recommender system.