📽 [Virtual Talk] Building a Resilient, Real-Time Fraud System at Block

Data is crucial for AI/ML systems but often becomes a bottleneck in development. 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 at Tecton, will explore how declarative frameworks are a game changer for production AI, empowering data scientists and ML engineers to collaborate effortlessly and build production features with ease. 

Sergio will demonstrate how Tecton’s declarative framework streamlines collaboration between data scientists and ML engineers, automates feature materialization, and handles diverse data types. Discover strategies to promote feature reusability, eliminate training-serving skew, and simplify complex feature creation. He’ll also discuss how these frameworks drive automation of production-ready pipelines, accelerating AI projects and making AI-powered applications smarter.

Key topics include:

• Overcoming traditional feature engineering challenges

• Real-world applications and success stories

• Strategies for efficient feature reusability and scalability