SageMaker Studio, Delta Lake, and MLflow Series

Overview

This repository contains a workshop series demonstrating the integration of Delta Lake and MLflow with AWS SageMaker Studio. It covers key workflows such as using Delta Lake for data management and applying MLflow for experiment tracking, model management, and deployment within the SageMaker environment.

Contents

• 01a-Delta Lake Workshop - Delta Lake Primer.ipynb: Introduction to Delta Lake, its architecture, and how it integrates with data workflows.
• 01b-Delta Lake Workshop - SageMaker Models.ipynb: How to leverage SageMaker models with Delta Lake and manage experiments using MLflow.
• helper/: Contains utility scripts for the workshop.
• img/: Contains images and diagrams for the notebooks.

Requirements

• AWS SageMaker Studio
• Delta Lake
• MLflow
• Python 3.x

Install dependencies:

pip install -r requirements.txt

Usage

Delta Lake Primer

• Navigate to 01a-Delta Lake Workshop - Delta Lake Primer.ipynb and run the notebook to understand how to set up and manage Delta Lake within SageMaker Studio.

SageMaker Models with Delta Lake

• Navigate to 01b-Delta Lake Workshop - SageMaker Models.ipynb to explore how to integrate Delta Lake with machine learning models on SageMaker, and how to track experiments using MLflow.

License

This project is licensed under the MIT License. See the LICENSE file for more information.