Strengths
- Open-source with a strong community and continuous development.
- Comprehensive end-to-end ML lifecycle management on Kubernetes.
- Supports multiple ML frameworks and tools for flexibility.
- Scalable and portable across different cloud and on-prem environments.
- Modular architecture allows customization and extensibility.
Limitations
- Steep learning curve for users unfamiliar with Kubernetes.
- Complex setup and configuration compared to simpler MLOps tools.
- Documentation can be fragmented due to rapid development and multiple components.