Data is at the heart of ML—training a traditional supervised model is impossible without access to high-quality historical data, which is commonly stored in a data lake. Delta Lake is an open-source storage framework that enables building a Lakehouse architecture with compute engines, including Spark, PrestoDB, Flink, Trino, and Hive, and APIs for Scala, Java, Rust, Ruby, and Python.
Amazon SageMaker is a fully managed service that provides a versatile workbench for building ML solutions and provides highly tailored tooling for data ingestion, data processing, model training, and model hosting.
Combining SageMaker Studio and Delta Lake brings state-of-the-art machine learning to your data lake. In this session, we show how you can train ML models and how you can take advantage of the capabilities offered by Delta Lake using Amazon SageMaker Studio.