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Introduction to the JFrogML Feature Store
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Course Presentation
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Why Use a Feature Store?
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Demo - Configuring a Data Source
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Demo - Configuring an Ingestion Pipeline
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Demo - Consuming Features - offline
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Demo - Consuming Features - online
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Final Summary & Key Points
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Managing Machine Learning Data with the JFrog ML Feature Store
Course 3 of 3 in JFrog ML
This course discover how the JFrog ML Feature Store can simplify and accelerate your machine learning workflows. In this hands-on course, you'll learn to manage, transform, and serve feature data efficiently - online and offline. Join us and see how to get the most out of your ML data with powerful tools, flexible pipelines, and low-latency performance.
Welcome to Managing Machine Learning Data with the JFrog ML Feature Store!
In this course, you’ll Learn how to streamline your ML workflows with components designed for data ingestion, transformation, and storage.
Through hands-on demonstrations, you'll explore how to configure data sources and build feature ingestion pipelines, while gaining insight into the role of metadata and the platform’s flexible compute options.
The course also covers best practices for querying and consuming feature data using both offline and online stores, emphasizing low-latency operations and the standalone power of the JFrogML SDK.
What You’ll Learn:
- Introduction to the JFrogML Feature Store
- Benefits of Using a Feature Store
- Data Ingestion and Transformation
- Metadata Management
- Compute and Storage Flexibility
- Offline and Online Feature Stores
- Using the JFrogML SDK
- Live Demonstration and Best Practices