IBM Cloud Pak® for Data Version 4.6 will reach end of support (EOS) on 31 July, 2025. For more information, see the Discontinuance of service announcement for IBM Cloud Pak for Data Version 4.X.
Upgrade to IBM Software Hub Version 5.1 before IBM Cloud Pak for Data Version 4.6 reaches end of support. For more information, see Upgrading IBM Software Hub in the IBM Software Hub Version 5.1 documentation.
Getting started with building, deploying, and trusting models
To get started with building, deploying, and trusting models, understand the overall workflow, choose a tutorial, and check out other learning resources for working with Watson Studio in Cloud Pak for Data.
Overview of the model workflow
The model workflow has three main steps: build a model asset, deploy the model, and build trust in the model.
Build a model asset
- Create a project.
- Add data to the project. If necessary, prepare your data.
- Choose a tool to build a model. You can choose from code editors, graphical builders, or automatic tools.
Deploy the model
- Create a deployment space and add the model to it.
- Deploy and score the model, and review prediction scores and insights.
- Monitor deployment jobs in a dashboard.
Build trust in your models
- Evaluate your deployment for bias or drift.
- Update your data and retrain the model until you reach your quality goals.
- Update deployments with better-performing models.
- Continue to evaluate, retrain, and update the deployed model.
Tutorials
This tutorial provides a description of the tool, a video, the instructions, and additional learning resources:
| Tutorial | Description | Expertise for tutorial |
|---|---|---|
| Build and deploy a machine learning model with AutoAI | Automatically build model candidates with the AutoAI tool. | Build, deploy, and test a model without coding. |
| Build and deploy a machine learning model in a notebook | Build a model by updating and running a notebook that uses Python code and the Watson Machine Learning APIs. | Build, deploy, and test a scikit-learn model using Python code. |
| Build and deploy a machine learning model with SPSS Modeler | Build a C5.0 model using the SPSS Modeler tool. | Drop data and operation nodes on a canvas and select properties. |
| Build and deploy a Decision Optimization model | Automatically build scenarios with the Modeling Assistant. | Solve and explore scenarios, then deploy and test a model without coding. |
Learning resources
Documentation
Videos
- A comprehensive set of videos that show many common machine learing tasks in Cloud Pak for Data.
Training
- Watson Studio Methodology {: new_window}
is an IBM Training e-Learning course that provides an in-depth look at Watson
Studio. - Take control of your data with Watson Studio {: new_window} is a learning path that consists
of step-by-step tutorials that explain the process of working with data using Watson Studio.
- Build models using Jupyter Notebooks in IBM Watson Studio is a tutorial that explains how to set up, run, and deploy Jupyter Notebooks.