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Deploying and managing machine learning assets

Deploying and managing machine learning assets

Use Watson Machine Learning to deploy models and solutions so that you can put them into productive use, then monitor the deployed assets for fairness and explainability. You can also automate the AI lifecycle to keep your machine learning assets current.

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.

Overview of model workflow

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 that you need to deploy an asset, such as 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.

Finally, you can use IBM Watson Pipelines to manage your ModelOps processes. Create a pipeline that automates parts of the AI lifecycle, such as training and deploying a machine learning model.

Use cases and tutorials

Watson Machine Learning is part of IBM's data fabric collection of tools and capabilities for managing and automating your data and AI lifecycle. These resources demonstrate how to plan for managing machine learning assets and how to build key pieces of your Data Fabric and machine learning solutions.

For details on how data fabric can support your machine learning goals and operations in practical ways, see Data fabric solution overview.

Next steps

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