Amazon SageMaker with Snowflake offer a robust set of features for preparing data, training models and deploying models. In this hands-on lab, we will provide an introduction to SageMaker, then discuss and demonstrate the common patterns for preparing training data, building models and deploying those models using SageMaker.

You will learn about:
  • Snowflake data management features for machine learning
  • How to leverage data in Snowflake’s Data Marketplace
  • How to connect SageMaker Data Wrangler and Studio to Snowflake
  • The analysis and feature engineering capabilities in Data Wrangler
  • Building and deploying SageMaker Pipelines
  • Options to integrate the ML models and pipeline with Snowflake
What you will build:
  • A Snowflake database for machine learning and data enrichment using the Data Marketplace
  • SageMaker Studio environment with integration to Snowflake
  • SageMaker Data Wrangler flow with Snowflake data
  • SageMaker Pipeline to prep Snowflake data and perform inference
Hands-on lab prerequisites:
  • Familiarity with Snowflake and a Snowflake account
  • Familiarity with SageMaker and an AWS account
  • Familiarity with Python


In Collaboration with :

  • Venkat Suru

    Principal Data Platform Architect, Field CTO Office

  • Hazirah Hasnan

    Solutions Architect