Reduce time spent on manual tasks and increase model performance.
View your machine learning development in an intuitive decision tree that enables better collaboration & knowledge sharing with your team.
trail uses LLMs to generate accurate documentation of your ML development from code, data and model. Saving you hours of time.
Metrics, parameters and artifacts of your last model run
Sources, metrics and distributions of your dataset
more time for coding
Analysis and aggregation of information
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.
View dataset statistics and metrics for each experiment on the fly to make sure you have all optimization factors in plain sight.
Code recommendations for testing and quality improvement, so that you keep control over the output.
Install our python package by simply adding a few lines of code
Stay in your IDE and experiment as you would normally do
Any metadata from ML development automatically feeds into the trail UI
Analyze, compare, export and report on runs, data and development decisions
Keep using what you like - trail integrates in your infrastructure and model frameworks