Deploying and managing AI assets
Use IBM Watson Machine Learning to deploy and manage AI assets, and put them into pre-production and production environments. Manage and update deployed assets. You can also automate part of the AI lifecycle using IBM Orchestration Pipelines.
Deploying AI assets and orchestrating pipelines
Deploying an asset makes it avaiable for testing or for productive use via an endpoint.
The following graphic describes the process of deploying your model, automating path to production, and monitoring and managing AI lifecycle after you build your model:
Deploy assets
You can deploy assets from your deployment space by using Watson Machine Learning. To deploy your assets, you must promote these assets from a project to your deployment space or import these assets directly to your deployment space. You can also use watsonx.ai to deploy tuned foundation models and prompt templates.
For more information, see Deploying AI assets.
Automate pipelines
You can automate the path to production by building a pipeline to automate parts of the AI lifecycle from building the model to deployment by using Orchestration 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.
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.
Learn more
Parent topic: Analyzing data and working with models