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Workshop: Building Real-Time ML Features with Feast, Spark, Redis, and Kafka

Posted Apr 12
# Data engineering
# Feature Stores
# Open Source
# Systems and Architecture
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SPEAKER
Danny Chiao
Danny Chiao
Danny Chiao
Engineering Lead @ Tecton

Danny Chiao is an engineering lead at Tecton/Feast Inc working on building a next-generation feature store. Previously, Danny was a technical lead at Google working on end to end machine learning problems within Google Workspace, helping build privacy-aware ML platforms / data pipelines and working with research and product teams to deliver large-scale ML powered enterprise functionality. Danny holds a BachelorÕs degree in Computer Science from MIT.

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Danny Chiao is an engineering lead at Tecton/Feast Inc working on building a next-generation feature store. Previously, Danny was a technical lead at Google working on end to end machine learning problems within Google Workspace, helping build privacy-aware ML platforms / data pipelines and working with research and product teams to deliver large-scale ML powered enterprise functionality. Danny holds a BachelorÕs degree in Computer Science from MIT.

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SUMMARY

This workshop will focus on the core concepts underlying Feast, the open source feature store. WeÕll explain how Feast integrates with underlying data infrastructure including Spark, Redis, and Kafka, to provide an interface between models and data. WeÕll provide coding examples to showcase how Feast can be used to:

  • Curate features in online and offline storage

  • Process features in real-time

  • Ensure data consistency between training and serving environments

  • Serve feature data online for real-time inference

  • Quickly create training datasets

  • Share and re-use features across models

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Posted Apr 12 | Views 12
# Data engineering
# Feature Stores
# Production Use Case
# Systems and Architecture