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 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 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.

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.

AI governance

The following tutorials use the scenario for Golden Bank, that needs a model to identify whether customers qualify for mortgages to reduce the bank’s application processing costs.

AI governance use cases
Use case	Description
Build and deploy a model	Train a model, prompt it to a deployment space, and deploy the model.
Test and validate the model	Evaluate a model for quality, fairness, drift, and explainability.

Data Science and MLOps

The following tutorials use the scenario for Golden Bank, that needs to automate a data pipeline that delivers up-to-date data on all mortgage applicants, that lenders can use to make decisions.

Data Science and MLOps use cases
Use case	Description
Orchestrating an AI pipeline with model monitoring	Create an end-to-end pipeline that transforms data and trains a model.
Orchestrating an AI pipeline with data integration	Create an end-to-end pipeline to prepare your data, train your model, and validate your model.

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Parent topic: Analyzing data and working with models

Deploying and managing AI assets

Deploying AI assets and orchestrating pipelines

Managing AI lifecycle with ModelOps

Tutorials and use cases

Related quick start tutorials

Related use cases

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