Integrating 3rd-party ML engines with Watson OpenScale

IBM Watson OpenScale integrates with external machine learning service engines, such as Microsoft Azure ML Studio, Microsoft Azure ML Service, and Amazon SageMaker.

You can integrate Watson OpenScale with 3rd-party engines in the following ways:

  • Engine binding level

    Ability to list deployments and get deployment details.

  • Deployment subscription level

    Watson OpenScale needs to be able to score subscribed deployment in the proper format, such as the IBM Watson Machine Learning format and receive the output in the same compatible format. Watson OpenScale must be configured to process both input and output formats.

  • Payload logging

    Each input and output to the deployed model triggered by a user’s application must be stored in a payload store. The format of the payload records follows the same specification as mentioned in the preceding section on deployment subscription levels.

    Watson OpenScale uses those records to calculate bias, explanations, and others. It is not possible for Watson OpenScale to automatically store transactions that run on the user site, which is outside the Watson OpenScale system. This method is one of the ways that Watson OpenScale safeguards proprietary information. Use the Watson OpenScale Rest API or Python SDK to work with secure data.

Steps to implement this solution

  1. Learn about custom machine learning engines.
  2. Set up payload logging.
  3. Automate payload logging.
  4. Set up a custom machine learning engine by using one of these Custom machine learning engine examples.