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Building Operational ML applications is very complex Data is at the core of that complexity.
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Classroom Contents
Accelerating the ML Lifecycle with Enterprise-Grade Feature Stores
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- 1 Intro
- 2 Building Operational ML applications is very complex Data is at the core of that complexity.
- 3 Features are the signals we extract from data and are a critical part of any ML application.
- 4 Tecton is a data platform for ML applications
- 5 Managing sprawling and disconnected feature transform logic
- 6 Building high-quality training sets from messy data
- 7 Configuration-based training data set generation through simple APIs
- 8 1 Configure what features you want in a training dataset
- 9 Built-in row-level time travel for accurate training data
- 10 End-to-End Feature Lifecycle Management
- 11 Example: Automated Content Categorization in Jira
