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1
Intro
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Machine learning at Gojek
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Machine learning life cycle prior to Feast
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Problems with end-to-end ML systems
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Feast background
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Machine learning life cycle with Feast
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What is Feast?
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What is Feast not?
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Create entities and features using feature sets
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Ingesting a DataFrame into Feast
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Ingesting streams into Feast
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What happens to the data?
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Feature references and retrieval
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Events throughout time
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Ensuring point-in-time correctness
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Point-in-time joins
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Getting features for model training
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Getting features during online serving
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Feature validation in Feast
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Infer TFDV schemas for features
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Visualize and validate training dataset
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What value does Feast unlock?
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Roadmap
Description:
Explore how Gojek, Indonesia's first billion-dollar startup, leverages big data and machine learning to power decision-making across its diverse product offerings in this 38-minute talk. Discover the challenges in feature engineering for large-scale ML systems and learn how Feast, an open-source feature store built on Apache Spark and MLflow, addresses these issues. Gain insights into the impact of democratizing feature creation, sharing, and management on time-to-market and innovation. Examine the machine learning lifecycle before and after implementing Feast, understanding its role in overcoming data scaling and feature serving challenges. Delve into practical aspects of using Feast, including creating entities, ingesting data, ensuring point-in-time correctness, and validating features. Conclude with a look at the value Feast unlocks for organizations and its future roadmap.

Scaling Data and ML with Apache Spark and Feast - Feature Engineering for Production

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