ML Experiments Hub
Track, compare, and visualize machine learning experiments
Overview
ML Experiments Hub is your central workspace for tracking, comparing, and analyzing machine learning experiments. Say goodbye to spreadsheets and disorganized notebooks.
Core Features
- ✓Experiment Tracking: Automatically log parameters, metrics, and artifacts
- ✓Model Comparison: Side-by-side comparison of multiple experiments
- ✓Interactive Visualizations: Built-in plots for metrics, confusion matrices, and more
- ✓Version Control: Track models and datasets across iterations
- ✓Team Collaboration: Share experiments and insights with your team
Integrations
ML Frameworks
TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM
Notebooks
Jupyter, Google Colab, VS Code
Version Control
Git, GitHub, GitLab
Cloud Platforms
Cloud compute integration for remote training
API
REST API and Python SDK for custom integrations
Use Cases
Research Teams
Collaborate on experiments, share findings, and accelerate research
Data Science Teams
Track model iterations and optimize for business metrics
ML Engineers
Monitor production models and debug performance issues