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Data-centric Approach to Machine Learning Using Snowflake and Amazon SageMaker Data Wrangler

Whether you are building a startup or adding to a data-intensive application, taking advantage of a cloud data platform and machine learning (ML) technologies is key to innovating and gaining a competitive advantage. Snowflake and AWS (Amazon SageMaker) are bringing you a series of DevDay events to show how to build data-intensive applications with ML.

The success of your machine learning models and applications depends on both the data you have available and how you present the data from training to deployment. This is why data scientists can spend the majority of their time collecting data and transforming it into features. Learn from our instructors how Snowflake and Amazon SageMaker can help you accelerate the data improvement steps and easily get you from training to production.

In this lab, you’ll have the opportunity to:

Leverage existing Snowflake data and enrich it with the Snowflake Data Marketplace How to 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 Analyze data sets for bias that can impact ML models Perform quick analysis of features (data sets) for impact and relevancy on ML Models Steps for deploying the ML pipeline and integrating ML inference with Snowflake