Watson OpenScale Python client samples
Review and use sample Jupyter Notebooks that use the Watson OpenScale Python client library to demonstrate features and tasks.
When you use a sample notebook to demonstrate features and tasks with the Watson OpenScale Python client, you must be comfortable with coding in a Jupyter Notebook. A Jupyter Notebook is a web-based environment for interactive computing. You can run small pieces of code that process your data, and then immediately view the results of your computation. With sample Jupyter Notebooks, you can complete tutorials to demonstrate tasks such as building, training, and deploying models and configuring model evaluations.
Sample notebooks
View or run the following Jupyter notebooks to learn how to complete different tasks:
| Sample name | Tasks demonstrated |
|---|---|
| Working with Watson Machine Learning | Train, create and deploy a German Credit Risk model, configure Watson OpenScale to monitor that deployment, and inject seven days' worth of historical records and measurements for viewing in the Watson OpenScale Insights dashboard. |
| Working with SPSS Collaboration and Deployment services | Log payload for a model that's deployed on a custom model serving engine. |
| IBM Watson OpenScale and Batch Processing: Apache Spark on Cloud Pak for Data with IBM Analytics Engine | Enable quality and drift monitoring and run on-demand evaluations with IBM Analytics Engine |
| IBM Watson OpenScale and Batch Processing: Remote Spark | Enable quality and drift monitoring and run on-demand evaluations with Remote Spark |
| OpenScale Model Risk Governance with OpenPages Integration on IBM Cloud Pak for Data | Integrate Watson OpenScale with IBM OpenPages and set up an end-to-end risk management solution. |
| OpenScale Model Risk Management on IBM Cloud Pak for Data | Set up a model risk management solution with Watson OpenScale. |
| Watson OpenScale indirect bias and active debiasing on IBM Cloud Pak for Data | Configure fairness evaluations to determine indirect bias. |
| Adversarial Robustness Metrics for image models | Use the Adversarial Robustness Toolkit (ART) to evaluate the robustness of image models. |
| Prompt template evaluation for RAG tasks with watsonx.governance | Create a prompt template asset for the RAG task and configure evaluations in watsonx.governance projects and spaces. |
Next steps
- To learn more about using notebook editors, see Notebooks.
- To learn more about working with notebooks, see Coding and running notebooks.
- To learn more about authenticating in a notebook, see Authenticating.