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 :