Better Features for Real-time Decisions Using Feature Engines


Transforming raw data into features to power machine learning models is one of the biggest challenges in production ML.

Tecton CEO, Mike Del Balso, will explain how leading ML teams use feature platforms to develop, operate, and manage features for production ML.

He'll walk through a sample use case, demonstrate how feature engines can make it easy to build & productionize powerful feature pipelines, and explain how an optimized feature engineering framework can enable:

☑️ Quick data processing,
☑️ Low-latency data serving,
☑️ Significantly reduced storage and computation costs,
☑️ Consistency between offline and online data for enhanced model accuracy.

Following Mike's talk, Tecton Developer Advocate, Nick Acosta, will take attendees through a hands-on workshop of Tecton where they'll walk through the concepts and code that will help you build a modern technical architecture that simplifies the process of managing real-time ML models and features.

Through a series of code examples, this virtual hands-on lab, you will learn how to:

Understand feature stores and feature platforms and their place in the machine learning lifecycle
Use the Tecton API to build a variety of Features to power a machine learning model
Utilize Tecton and Databricks together to power real-time machine learning


Nick Acosta enjoys helping developers automate feature pipelines as a Developer Advocate at Tecton, a feature platform for real-time machine learning. Nick has previously led Developer Relations teams at Fivetran and IBM.

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