Description
Neptune is a metadata store for MLOps that enables data science teams to log, store, display, organize, compare, and query all model-building metadata. The platform helps teams track experiments, monitor model performance in production, and establish robust model governance practices. By providing comprehensive visibility into the machine learning lifecycle, Neptune improves collaboration, reproducibility, and the overall quality of AI initiatives while helping organizations maintain compliance with regulatory requirements.
Key Features
- Comprehensive experiment tracking
- Model registry and versioning
- Model monitoring in production
- Team collaboration capabilities
- Metadata organization and querying
Use Cases
- Machine learning experiment management
- Model governance and compliance
- Research collaboration
- ML process optimization
- Model performance tracking
Pricing Model
Tiered subscription with free tier for individuals
Integrations
Major ML frameworks, Cloud platforms, CI/CD pipelines, Development environments, Notebooks and IDEs
Target Audience
Data science teams, ML engineers, MLOps specialists, AI researchers, Model governance stakeholders
Launch Date
2018
Available On
Cloud-based platform, On-premises deployment, Python SDK
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