You can deploy your AI assets in watsonx.ai Runtime programmatically by using watsonx.ai Python client library, watsonx.ai Runtime API, and IBM Cloud Pak for Data Command Line Interface (IBM cpdctl).
Deploying assets with watsonx.ai Python client library
Use the watsonx.ai Python client library to work with traditional machine learning and generative AI assets to train, store, and deploy your models. You can score your models by using the APIs and integrate them with your application deployment. To learn more about the installation, setup, and usage, see ibm-watsonx-ai library documentation.
The watsonx.ai Runtime Python client library (ibm-watson-machine-learning) is now part of the expanded watsonx.ai Python client library
(ibm-watsonx-ai). The watsonx.ai Runtime library will persist but will not be updated with new features.
Sample notebooks
You can refer to sample notebooks that demonstrate machine learning features and techniques.
For more information, see watsonx.ai Runtime Python client samples and examples.
Deploying assets with APIs
You can use the watsonx.ai API, watsonx.ai Runtime API, and Data and AI Common Core API to build models and deploy them for use in applications. Choose from tools that fully automate the training process for rapid prototyping to tools that give you complete control to create a model that matches your needs. You can access the watsonx.ai Runtime REST API from the Python client library.
Use the watsonx.ai Runtime API to deploy AutoAI experiments, models, trainings, and pipelines and more.
For more information, see watsonx.ai Runtime API documentation.
Use the Data and AI Common Core API to deploy and manage data-related assets such as assets, catalogs, environments, hardware specifications, software specifications, runtime definitions, and more.
For more information, see Data and AI Common Core API.
Deploying assets with IBM Cloud Pak for Data Command Line Interface
Use the IBM Cloud Pak for Data Command Line Interface (cpdctl) to manage the configuration settings and automate the lifecycle of AI assets. You can create or delete projects, change project hardware and software specifications, update package extensions, and manage access. You can also prepare and manage data assets and connections, create experiments, deploy models, and create pipelines.
For more information, see Managing AI lifecycle with cpdctl.
Authenticating for programmatic access
You must authenticate to use watsonx.ai Runtime securely by using a token or credentials. The preferred method to authenticate with a token is by using ZenApiKey. If you are not storing your notebook within watsonx.ai Studio, you can bypass retrieving a token and authenticate with your Cloud Pak for Data credentials.
For more information, see Authenticating for programmatic access.
Learn more
- Authenticating for programmatic access
- watsonx.ai Runtime Python client samples and examples
- watsonx.ai Runtime API
Parent topic: Deploying AI assets