Tutorial: Monitor Your Deployed WML Model with Watson OpenScale

1 min read

By: Vidyasagar Machupalli

IBM Watson OpenScale allows enterprises to automate and operationalize the AI lifecycle in business applications.

This ensures that AI models are free from bias, can be easily explained and understood by business users, and are auditable in business transactions. Watson OpenScale supports AI models built and run in the tools and model serve frameworks of your choice. Watson OpenScale supports AI models built and run in the tools and model serve frameworks of your choice.

IBM Watson OpenScale

With Watson OpenScale, you can monitor model quality and log payloads, regardless of where the model is hosted. IBM Watson OpenScale is an open environment that enables organizations to automate and operationalize their AI. OpenScale provides a powerful platform for managing AI and machine learning (ML) models on the IBM Cloud (or wherever they may be deployed), offering these benefits: 

  • Open-by-design: Watson OpenScale allows monitoring and management of ML and DL models built using any frameworks or IDEs and deployed on any model hosting engine. 
  • Drive fairer outcomes: Watson OpenScale detects and helps mitigate model biases to highlight fairness issues. The platform provides plain text explanation of the data ranges that have been impacted by bias in the model and visualizations that help data scientists and business users understand the impact on business outcomes. As biases are detected, Watson OpenScale automatically creates a de-biased companion model that runs beside deployed model, thereby previewing the expected fairer outcomes to users without replacing the original. 
  • Explain transactions: Watson OpenScale helps enterprises bring transparency and auditability to AI-infused applications by generating explanations for individual transactions being scored, including the attributes used to make the prediction and weightage of each attribute.

Tutorial: Building, deploying, testing, monitoring, and retraining a model

A tutorial with step-by-step instructions is drafted that walks you through the end-to-end process of the following:

  • Building a predictive machine learning model 
  • Deploying it as an API to be used in applications
  • Testing the model
  • Monitoring the model using Watson OpenScale 
  • Retraining the model with feedback data 

All of this happening in an integrated and unified self-service experience on IBM Cloud.

Tutorial: IBM Watson OpenScale

In this tutorial, the Iris flower data set is used for creating a machine learning model to classify species of flowers.

Following the steps in the tutorial, you will create a IBM Watson OpenScale service to monitor the health, performance, accuracy, and quality metrics of your machine learning model deployed to Watson machine learning (WML) service on IBM Cloud, along with throughput and analytics.

Check out the full tutorial.

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