Main Purpose
The main purpose of Neptune.ai is to provide a platform for experiment tracking in the field of machine learning operations (MLOps).
Key Features
- Experiment Tracking: Neptune.ai offers a centralized place to log, compare, store, and collaborate on experiments and models in the field of machine learning.
- Visualization and Comparison: The platform provides visualizations and comparisons of metrics, hyperparameters, learning curves, and diagnostic charts.
- Integration with ML Ecosystem: Neptune.ai offers integrations with the machine learning ecosystem, allowing for easy logging of metadata generated during training runs.
- Artifact Management: The platform allows users to track and manage artifacts such as datasets, models, and their versions.
- Collaboration and Onboarding: Neptune.ai provides collaboration features for teams and an onboarding guide to help users get started with the platform.
Use Case
- Experiment Tracking: Neptune.ai is used by machine learning practitioners and teams to track and manage experiments, including metrics, hyperparameters, and model training metadata.
- Model Management: The platform helps in managing trained models, including tracking model binaries, dataset versions, and model descriptions.
- Collaboration and Onboarding: Neptune.ai facilitates collaboration among team members and provides an onboarding guide for new users.
