Data-Centric Machine Learning with Snowpark & Amazon SageMaker
Harness the power of the Data Cloud for your machine learning workloads. Using a data-centric approach to machine learning is key to innovating and gaining competitive advantage whether you are building a startup or adding to a data-intensive app.
Join Snowflake and AWS for an interactive half day session. In this workshop, you’ll learn how to take advantage of Snowpark for Python to transform your Snowflake data into features and then train a machine learning model using Amazon SageMaker. 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.
Target Audience
Data engineers looking to extend their core data workloads with feature engineering and machine learning (ML). ML Practitioners looking to take advantage of Snowflake’s highly scalable compute and data capabilities in their ML workflow. Data Scientists looking to combine data from different sources for feature engineering.
What you’ll learn
The exercises in this lab will walk you through the steps to:
- Configure SageMaker Studio for use with Snowpark for Python
- Load and transform data with pushdown computation via Snowpark
- Train a machine learning model using Python in a SageMaker Studio Notebook
- Deploy your model a User Defined Function (UDF) for distributed scoring in Snowflake
Prerequisites
- Basic knowledge of SQL, and database concepts and objects
- Familiarity with CSV comma-delimited files
- Basic Jupyter notebook and Python knowledge
We look forward to seeing you there! Save your seat now!
In collaboration with:
Data-Centric Machine Learning with Snowpark & Amazon SageMaker
Harness the power of the Data Cloud for your machine learning workloads. Using a data-centric approach to machine learning is key to innovating and gaining competitive advantage whether you are building a startup or adding to a data-intensive app.
Join Snowflake and AWS for an interactive half day session. In this workshop, you’ll learn how to take advantage of Snowpark for Python to transform your Snowflake data into features and then train a machine learning model using Amazon SageMaker. 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.
Target Audience
Data engineers looking to extend their core data workloads with feature engineering and machine learning (ML). ML Practitioners looking to take advantage of Snowflake’s highly scalable compute and data capabilities in their ML workflow. Data Scientists looking to combine data from different sources for feature engineering.
What you’ll learn
The exercises in this lab will walk you through the steps to:
- Configure SageMaker Studio for use with Snowpark for Python
- Load and transform data with pushdown computation via Snowpark
- Train a machine learning model using Python in a SageMaker Studio Notebook
- Deploy your model a User Defined Function (UDF) for distributed scoring in Snowflake
Prerequisites
- Basic knowledge of SQL, and database concepts and objects
- Familiarity with CSV comma-delimited files
- Basic Jupyter notebook and Python knowledge
We look forward to seeing you there! Save your seat now!
Snowflake Customer Experience Centre
20 June 2023
14:00 – 17:00pm