This article demonstrates an end-to-end ML workflow integrating Amazon Redshift data warehouse with Amazon SageMaker Feature Store for building ML features at scale.

• Three options for feature engineering: AWS Glue interactive sessions, SageMaker Processing with Spark, or Data Wrangler
• RedshiftDatasetDefinition enables simple Redshift connection configuration without maintaining persistent connections
• SageMaker Feature Store Spark connector allows distributed feature ingestion from Spark DataFrames
• DatasetBuilder API and Athena queries enable ML dataset creation from feature groups
• Batch transform provides bulk model inference on S3 data with results stored in S3
• Amazon Redshift Spectrum joins batch predictions with original Redshift data
• CloudFormation template automates setup of SageMaker domain, Redshift cluster, and required IAM roles

The post provides a complete guide for data scientists to seamlessly move data from Redshift through feature engineering to model training and inference using AWS native services.