Supercharge Production AI with Features as Code

Data is the backbone of AI/ML systems, but it is often the bottleneck in the development process. Data scientists and engineers grapple with the complexity of building and maintaining feature pipelines, ensuring consistency, data freshness, and achieving real-time performance.

In this talk, Sergio Ferragut, Principal Developer Advocate, will explore how Tecton's declarative framework empowers data scientists and ML engineers to collaborate effortlessly and build production features with ease. We'll dive into the key components of the framework and showcase how it automates the materialization of features, handles both batch, streaming and real-time data, as well as driving context for generative AI while ensuring consistency between training and serving.

We'll show how Tecton's declarative framework:

1. Enables seamless collaboration between data scientists and ML engineers
2. Promotes reusability and shareability of features across projects
3. Eliminates training-serving skew
4. Simplifies the creation of complex features with aggregations and time windows
5. Drives Tecton’s automation of production ready feature pipelines

And we will show how it all works under the hood.

This talk will provide you with practical insights to leverage Tecton's declarative framework to get to market faster, build smarter applications, and lower production costs. Join us to discover how Tecton can help you unlock the full potential of your data for AI.