In this code pattern, we use German credit data to train, create, and deploy a machine learning model using Watson Machine Learning. We create a data mart for this model with [Watson OpenScale] and configure OpenScale to monitor that deployment, and inject seven days’ worth of historical records and measurements for viewing in the OpenScale Insights dashboard.
When you have completed this code pattern, you will understand how to:
- Create and deploy a machine learning model using the Watson Machine Learning service
- Set up a Watson OpenScale data mart
- Bind Watson Machine Learning to the Watson OpenScale data mart
- Add subscriptions to the data mart
- Enable payload logging and performance monitoring for subscribed assets
- Enable Quality (Accuracy) monitoring
- Enable Fairness monitoring
- Score the German credit model using Watson Machine Learning
- Insert historical payloads, fairness metrics, and quality metrics into the data mart
- Use the data mart to access tables data through subscription
- The developer creates a Jupyter Notebook on Watson Studio.
- The Jupyter Notebook is connected to a PostgreSQL database, which is used to store Watson OpenScale data.
- The notebook is connected to Watson Machine Learning and a model is trained and deployed.
- The notebook uses Watson OpenScale to log payload and monitor performance, quality, and fairness.
For detailed instructions, please see the README.