Deploying and managing AI assets

Last updated: Jan 23, 2025

Use IBM watsonx.ai Runtime 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 available 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:

AI lifecycle

Deploy assets

You can deploy assets from your deployment space by using watsonx.ai Runtime. 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.

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.

Review quick start tutorials to learn how to build and deploy machine learning models:

Quick start tutorials
Tutorial	Description
Build and deploy a machine learning model	Build a machine learning model by using a no-code approach with an automated tool, AutoAI. Deploy the trained model by creating a batch deployment. Score your batch deployment by creating a batch job.
Build and deploy a machine learning model in Jupyter Notebook	Build a machine learning model by using code in a Jupyter notebook to prepare data, build your model, and deploy your model.
Build and deploy a machine learning model with SPSS Modeler	Build a machine learning model by using a no-code approach by using an SPSS Modeler flow. Deploy the trained model by creating an online deployment and test the model in real-time.
Build and deploy a Decision Optimization model	Build a machine learning model by using Decision Optimization.
Automate the lifecycle for a model with pipelines	Create and run a pipeline to automate building and deploying a machine learning model.

The following tutorial uses the scenario for Golden Bank, that needs a model to perform stock anomalies analysis to boost productivity and increase the accuracy of a stock analyst's work in investment banking.

watsonx.ai use cases
Use case	Description
End-to-end watsonx.ai tutorial	Build your model using any of the tools, such as AutoAI, SPSS Modeler, or Jupyter notebooks, followed by deploying and testing your model. Further, transform your data and tune your foundation model by using watsonx.ai.

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Parent topic: Analyzing data and working with models

Deploying AI assets and orchestrating pipelines

Tutorials and use cases

Related quick start tutorials

Related use cases

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