Michelangelo-AI

Michelangelo-AI is an open-source platform designed to streamline the development, deployment, and monitoring of machine learning models at scale. It offers a comprehensive suite of tools and services that facilitate the entire machine learning lifecycle, from data management to model serving.

:warning: Beta Notice  This project is currently in beta. APIs and features may evolve, and breaking changes may occur as we continue to improve and stabilize the platform.

Open Source Initiative

As part of the AI Platform Open Source team, we are extending these capabilities beyond Uber by open-sourcing an end-to-end lifecycle management system grounded in our extensive operational expertise. Our goals are to:

• Drive standardization and interoperability across the ML ecosystem,
• Enable easy adoption of scalable ML solutions in new production use cases,
• Foster innovation and trust through collaboration with partner teams, and
• Cultivate a vibrant and responsible ML culture that empowers the community to build with confidence and speed.

We are incrementally open-sourcing Michelangelo’s core capabilities, ensuring each release is production-proven and developer-ready. The documentation on this site reflects the current set of available features and will be continuously updated as new components are added to the open-source repository.

Features

• Feature Management: Efficiently handle large datasets with built-in support for data ingestion, transformation, and storage.
• Model Training: Train models using various algorithms, including support for distributed training across multiple nodes.
• Model Evaluation: Assess model performance with a range of metrics and visualization tools.
• Model Deployment: Seamlessly deploy models to production environments with support for both batch and real-time inference.
• Monitoring and Logging: Continuously monitor model performance and log predictions to ensure reliability and accuracy.

Installation

To install Michelangelo-AI, follow Sandbox Setup guide until GitHub respository is public.

Usage

Here's a basic example of how to train and deploy a model using Michelangelo-AI:

1. Data Preparation: Load and preprocess your dataset.
2. Model Training: Use the training module to train your model.
3. Model Evaluation: Evaluate the trained model's performance.
4. Model Deployment: Deploy the model to the production environment.

For detailed instructions and advanced usage, refer to the Michelangelo-AI Docs.

Build and Test

Please refer to the User Guides in the documentation.

Consuming and Using the Containers

Please refer to the Sandbox Setup section of the documentation.

Contributing

We welcome contributions to Michelangelo-AI!
If you're interested in contributing, please read our Contributing Guidelines to get started.

License

This project is licensed under the Apache 2.0 License before public release.

Acknowledgments

We would like to thank all the contributors to this project.