This instructor-led hands-on lab will guide you through the data-centric approach for machine learning, along with the benefits of acquiring relevant data, evaluating and using this data to build ML models.

You’ll learn how to:
  • Leverage existing Snowflake data and enrich it with the Snowflake Data Marketplace
  • Use Snowflake’s Zero Copy Cloning to maintain ML Training Data Sets Use SageMaker Data Wrangler with Snowflake as a data source
  • Perform GUI-based feature engineering
  • Analyse data sets for bias that can impact ML models
  • Perform quick analysis of features (data sets) for impact and relevancy on ML Models
  • Initiate steps for deploying the ML pipeline and integrating ML inference with Snowflake
Lab Prerequisites:
  • Snowflake Enterprise Account on AWS with ACCOUNTADMIN access – Snowflake Trial Account will work
  • AWS Account with admin access – (AWS Credits will be provided)
  • In the AWS account create a VPC and subnets in the region similar to the Snowflake account
  • Andries Engelbrecht

    Partner Solutions Architect, Snowflake

  • Dylan Tong

    Global Tech Lead, Decision Intelligence, AWS