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Building a Movie Recommendation System on Tecton with Snowflake

Posted Jun 14
# Tecton
# Snowflake
# Movie Recommendation System
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SPEAKER
David Hershey
David Hershey
David Hershey
Solutions Engineer @ Tecton

David Hershey is a solutions engineer for Tecton. David has a passion for machine learning infrastructure, in particular systems that enable data scientists to spend more time innovating and changing the world with ML. Previously, David worked at Ford Motor Company as an ML Engineer where he led the development of Ford's ML platform. He received his MS in Computer Science from Stanford University, where he focused on Artificial Intelligence and Machine Learning.

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David Hershey is a solutions engineer for Tecton. David has a passion for machine learning infrastructure, in particular systems that enable data scientists to spend more time innovating and changing the world with ML. Previously, David worked at Ford Motor Company as an ML Engineer where he led the development of Ford's ML platform. He received his MS in Computer Science from Stanford University, where he focused on Artificial Intelligence and Machine Learning.

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SUMMARY

Tecton integrates with Snowflake and enables data teams to process ML features and serve them in production quickly and reliably, without building custom data pipelines.

David shows how to build an end-to-end movie recommendation system using a feature platform in three stages:

  • Batch, daily computed, recommendations
  • Online recommendations using batch features
  • Online recommendations using real-time features
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