Deploying models as endpoints#

Data Science & AI Workbench enables you to deploy machine learning models as endpoints to make them available to others, so the models can be queried and scored. You can then save users’ input data as part of the training data, and retrain the model with the new training dataset.

Versioning your model:

To enable you to test variations of a model, you can deploy multiple versions of the model. You can then direct different sets of users to each of the versions, to facilitate A/B testing.

Deploying your model as an endpoint:

Deploying a model as an endpoint involves these simple steps:

  1. Create a project to tell Workbench where to look for the artifacts that comprise the model.

  2. Deploy the project to build the model and all of its dependencies. Now you—and others with whom you share the deployment—can interact with the app, and select different datasets and algorithms.