The Copilot for Efficient ML Development
Reduce time spent on manual tasks and increase model performance.
Visualization
View your machine learning development in an intuitive decision tree that enables better collaboration & knowledge sharing with your team.
Automated Documentation
trail uses LLMs to generate accurate documentation of your ML development from code, data and model.
Saving you hours of time.
Model
Metrics, parameters and artifacts of your last model run
Data
Sources, metrics and distributions of your dataset
more time for coding
Code
Analysis and aggregation of information
Experiment Tracking
Track and compare all relevant metadata & artifacts of your machine learning models and datasets to finally have one single source of truth for your ML metadata.
All Data & Model Types
Data-Centric ML
View dataset statistics and metrics for each experiment on the fly to make sure you have all optimization factors in plain sight.
Tests & Refactoring
Code recommendations for testing and quality improvement, so that you keep control over the output.
How it works
1. Add code
Install our python package by simply adding a few lines of code
2. Run Experiments
Stay in your IDE and experiment as you would normally do
3. View Information
Any metadata from ML development automatically feeds into the trail UI
4. Improve Project
Analyze, compare, export and report on runs, data and development decisions
Seamlessly integrates into your workflow
Keep using what you like - trail integrates in your infrastructure and model frameworks
Get Started
