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Workshop: Operationalizing ML Features on Snowflake with Tecton

Posted Apr 12, 2022 | Views 674
# Data engineering
# Feature Stores
# Production Use Case
# Systems and Architecture
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SPEAKER
David Hershey
David Hershey
David Hershey
Senior Solutions Architect @ Tecton

David Hershey is a Solutions Architect at Tecton, where he helps customers implement feature stores as part of their stack for Operational ML. Prior to Tecton, David was a Solutions Engineer at Determined AI and a Product Manager for FordÕs ML platform. David holds an MS in Computer Science from Stanford University and BS in Aerospace Engineering from the University of Michigan.

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David Hershey is a Solutions Architect at Tecton, where he helps customers implement feature stores as part of their stack for Operational ML. Prior to Tecton, David was a Solutions Engineer at Determined AI and a Product Manager for FordÕs ML platform. David holds an MS in Computer Science from Stanford University and BS in Aerospace Engineering from the University of Michigan.

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SUMMARY

Many organizations have standardized on Snowflake as their cloud data platform. 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 and Miles will provide a demo of the Tecton and Snowflake integration along with coding examples. Attendees will learn how to:

  • Build new features using TectonÕs declarative framework

  • Automate the transformation of batch data directly on Snowflake

  • Automate the transformation of real-time data using Snowpark

  • Create training datasets from data stored in Snowflake

  • Serve data online using DynamoDB or Redis

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