📽 [Virtual Talk] Supercharge Production AI with Features as Code

Data is essential for AI/ML systems but often becomes a development bottleneck. Data scientists and engineers face challenges in building and maintaining feature pipelines, ensuring data consistency and freshness, and achieving real-time performance.

In this presentation, Sergio Ferragut, Principal Developer Advocate at Tecton, will discuss how declarative frameworks are transforming production AI. These frameworks enable seamless collaboration between data scientists and ML engineers, simplifying the creation of production features.

Sergio will show how Tecton’s declarative framework enhances collaboration, automates feature materialization, and supports diverse data types. Discover how to improve feature reusability, eliminate training-serving skew, and simplify complex feature development. He will also cover how these frameworks automate production-ready pipelines, speeding up AI projects and making AI-powered applications more intelligent.

Key topics include:

1. Seamless collaboration between data scientists and ML engineers

2. Reuse features and eliminate training-serving skew

3. Automation of streaming, batch and real-time feature pipelines