Data analyst selects a champion model after recording the experiment information generated during the model learning process. A dashboard provides an at-a-glance view of the execution history and status of each model.
Managing the entire lifecycle of an AI model, from managing versions and results of ML experiments for each model to comparing and verifying results, is easy.
A fully-managed repository records and shares the traits of the ML model for training and inferences. The recorded features can later be reused for training and inference, cutting the development process that could otherwise take months to a fraction of the time.
- View experiment execution list and current status : Running, Finished, Failed
- View the meta information of project : project ID, user ID, image
- Entire experiment list and detailed model history
- Manage entire experiments and view detailed information by model
- Compare and analyze past experiment history of models for experiment management
- Model Verification function for model verification
- Provide Model Registry for managing and distributing model image