Deploying and evaluating assets

Use a suite of tools to deploy models and solutions so that you can put them into productive use, then monitor the deployed assets for fairness and explainability.

Completing the AI lifecycle

After you prepare your data and build then train models or solutions, you complete the AI lifecycle by deploying and monitoring your assets.

Deployment is the final stage of the lifecycle of a model or script, where you run your models and code. Watson Machine Learning provides the tools you need to deploy an asset, such as an SPSS Modeler flow, a machine learning model or function, or a Decision Optimization solution.

Following deployment, you can use model management tools to evaluate your models. IBM Watson OpenScale tracks and measures outcomes from your AI models, and helps ensure they remain fair, explainable, and compliant. Watson OpenScale also detects and helps correct the drift in accuracy when an AI model is in production.

Next steps

• Find out how to view and manage assets in deployment spaces
• Find out how to deploy assets from a deployment space.
• View sample notebooks that demonstrate deploying using the Python client or REST API.

• Evaluate your deployed models for bias using Watson Open Scale.

• Learn how to deploy Decision Optimization solutions