Deploying and managing AI assets

Use IBM Watson Machine Learning to deploy and manage AI assets, including models and generative AI assets, and put them into pre-production and production environments. You can monitor the deployed assets for fairness and explainability. Further, you can orchestrate the end-to-end flow of assets from creation through deployment by using IBM Watson Pipelines.

Deploying AI assets and orchestrating pipelines

Deploying your model to the production environment is a crucial step that you must follow after building or importing your model.

AI lifecycle

Deploy model

You can use Watson Machine Learning to deploy, monitor, and manage your AI assets by promoting your assets to a deployment space. You can also use watsonx.ai to deploy tuned foundation models and prompt templates. After deploying your model, you can evaluate your model within your deployment space for fairness, quality, and drift.

For more information, see Deploying AI assets.

Automate pipeline

You can automate the path to production by building a pipeline to automate parts of the AI lifecycle from building the model to deployment, and evaluate a model to shorten the time from conception to production by using Watson Pipelines.

For more information, see Orchestrating tasks with Pipelines.

Managing AI lifecycle with ModelOps

You can organize and manage assets through the development, testing, and production phase of the AI lifecycle. Further, you can share data assets in a feature store, automate ModelOps by using Pipelines, track models with AI factsheets, and evaluate model deployments.

For more information, see Managing the AI Lifecycle with ModelOps.

Tutorials and use cases

The following resources demonstrate how to plan for managing machine learning assets and how to build key pieces of your solutions.

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Parent topic: Analyzing data and working with models