Introduction
Trail is your Copilot for AI Governance. Trail automates the documentation of the Machine Learning development process, while bringing more transparency to your ML experiments and governance requirements. Start by using MLflow to track experiments and browse through the navigation from left to right to explore all the features.
Features
trail comes with the following main functionalities:
- AI Registry: trail allows you to register your use cases in a central place. This allows you to easily keep track of your AI use cases, compare risk levels and push adoption.
- Risk Management: trail helps you to identify and manage risks in your AI projects. It provides you with a set of risk templates, a risk assessment tool and a risk treatment planning to keep track of your risks.
- Experiment Management: trail brings traceability to your development process by visualizing tracked experiments in a clear tree structure. This allows you to easily compare different experiments, their evaluations and results.
- Documentation-Engine: trail creates automated documentations based on all the ML Metadata in a central place. This allows you to easily share your results with your interdisciplinary colleagues or external parties without the overhead of manually writing and updating documentation.
- Compliance: trail helps you to comply with the necessary regulations and standards in the field of AI. It breaks down the complicated governance requiremeents into easy to follow steps.
- Organizational Context: trail allows you to define the roles and responsibilities in your organization and to provide information in the organizational questionnaire. This information is important to get draft policies for certification.
For the full functionality, request access to trail.
What you need before getting started:
Trail app
You can access the trail app here and login with your credentials. If you don't have credentials yet, request early access on the website.
Python Package
You can find the trail python package on pypi.