Strengths
- Completely open-source with active community support
- Comprehensive end-to-end ML lifecycle management
- Supports hybrid and multi-cloud deployments
- Strong integration with popular ML frameworks and tools
- Scalable from individual researchers to large enterprises
Limitations
- Self-hosted setup can be complex for beginners
- Enterprise features require custom pricing and negotiation
- UI can be overwhelming for new users
- Limited built-in data labeling or annotation tools