- Overview
- Users
- Managing Models and Releases
- Uploading Artifacts
- Creating a Model
- Completing the Model
- Creating a Release
- Uploading Files
- Uploading Images
- Using a Model
- Requesting Access
- Personal Access Tokens
- Using a Pushed Docker Image
- Downloading files
- Reviews
- Reviewing Releases and Access Requests
- Reviewing a Release
- Reviewing an Access Request
- Reviewed Releases and Access Requests
- Releases
- Access Requests
- Programmatically using Bailo
- Authentication
- Open API
- Webhooks
- Python Client
- Administration
- Getting Started
- App Configuration
- Microservices
- File Scanners
- Helm
- Basic Usage
- Configuration
- Isolated Environments
- Schema
- Create a Schema
- Upload a Schema
- Migrations
- Bailo v0.4
- Bailo v2.0
- DataBase Scripts
What is Bailo?
Bailo provides a consistent, managed platform for the machine learning lifecycle, enabling models to be deployed in a standardised and well orchestrated way. This improves the use of ML models in operational contexts, and does so in a well controlled and low-risk manner.
The service aims to:
- Providing a centralised repository for machine learning models
- Encourage discovery and reuse of existing models
- Standardise model packaging and deployment
- Enforce consistent compliance and review processes
- Support monitoring and governance of operational models
All of this documentation is available on our GitHub repository. Contributions and corrections are welcome.
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