Model Pipelines
Model Pipelines allow data scientists to design, deploy, and govern end-to-end machine learning pipelines for seamless model development. Models in a workspace can be created or modified by a user with access to the workspace and model versions are preserved in the workspace along with execution and output histories. Once a model has been validated in the workspace and considered fit for use, modelers can request to publish the model into the production environment.
This seamless integration allows models to be developed, tested, and verified before being available in the production environment.
Steps to Enable
You don't need to do anything to enable this feature.
Key Resources