APIs for machine learning

You can manage spaces, deployments, and assets programmatically by using:

• watsonx.ai Runtime REST API
• The ibm-watsonx-ai Python library

For links to sample Jupyter Notebooks that demonstrate how to manage spaces, deployments, and assets programmatically, see Machine Learning Python client samples and examples.

APIs for AI Factsheets

You can manage settings, model entries, and report templates programmatically by using:

• AI Factsheets REST API
• AI Factsheets Python library

APIs for Watson OpenScale

You can manage data and settings for model evaluations programatically by using:

• Watson OpenScale REST API
• Watson OpenScale Python Library

APIs for managing assets and collaborators

You can manage data-related assets and collaborators by using the Data and AI Common Core API.

IBM Match 360 with Watson (Beta) API

You can add master data matching capabilities to your application by using the IBM Match 360 with Watson APIs.

Data Virtualization APIs

You can manage your virtual data, data sources, and user roles by using the Data Virtualization API.

DataStage APIs

You can manage your DataStage tasks by using:

• DataStage Flow APIs, such as Creation, Edit, and Delete
• Job APIs, such as Creation, Edit, Delete, and Run
• Migration APIs
• DataStage Flow Compile APIs

See the IBM APIs for DataStage.

Learn more

• watsonx.ai Runtime REST API docs

• The ibm-watson-machine-learning Python library docs

• AI Factsheets REST API docs

• AI Factsheets Python library docs

• Data and AI Common Core API docs

• Connections in the Data and AI Common Core API docs

• IBM Match 360 with Watson API docs

• Data Virtualization API docs

• DataStage API docs

Parent topic: Getting started