Streamline Machine Learning from Experimentation to Production
- A standardized end-to-end MLOps pipeline that tracks your ML projects from model experimentation to production deployment.
- Intuitive web dashboard with all logs, request metrics and machine metrics.
- Supports standard machine learning libraries like Tensorflow and Pytorch out of the box, easy to customize with additional dependencies.
Deploy to Production with One Command
- Kubernetes cluster management to save you time enabling model serving and autoscaling.
- Easily deploy models that you've trained on Spell or uploaded to the platform.
- Easy deployment with high performance load balancing and state of the art async web server.
Model Management and Versioning
- Full transparency with end-to-end lineage tracking that shows where and how your model was trained.
- An intuitive versioning feature that allows for faster experimentation of your models.
- Promote team collaboration by keeping model training details and notes all in one place.