Take quick start tutorials to learn how to perform specific tasks, such as refine data or build a model. These tutorials help you quickly learn how to do a specific task or set of related tasks.
The quick start tutorials are categorized by task:
Each tutorial requires one or more service instances. Some services are included in multiple tutorials. The tutorials are grouped by task. You can start with any task. Each of these tutorials provides a description of the tool, a video, the instructions, and additional learning resources.
The tags for each tutorial describe the level of expertise (, , or ), and the amount of coding required (, , or ).
After completing these tutorials, see the Other learning resources section to continue your learning.
Preparing data
To get started with preparing, transforming, and integrating data, understand the overall workflow, choose a tutorial, and check out other learning resources for working on the platform.
Your data preparation workflow has these basic steps:
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Create a project.
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If necessary, create the service instance that provides the tool you want to use and associate it with the project.
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Add data to your project. You can add data files from your local system, data from a remote data source that you connect to, data from a catalog, or sample data.
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Choose a tool to analyze your data. Each of the tutorials describes a tool.
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Run or schedule a job to prepare your data.
Tutorials for preparing data
Each of these tutorials provides a description of the tool, a video, the instructions, and additional learning resources:
| Tutorial | Description | Expertise for tutorial |
|---|---|---|
| Refine and visualize data with Data Refinery | Prepare and visualize tabular data with a graphical flow editor. | Select operations to manipulate data. |
Analyzing and visualizing data
To get started with analyzing and visualizing data, understand the overall workflow, choose a tutorial, and check out other learning resources for working with other tools.
Your analyzing and visualizing data workflow has these basic steps:
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Create a project.
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If necessary, create the service instance that provides the tool you want to use and associate it with the project.
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Add data to your project. You can add data files from your local system, data from a remote data source that you connect to, data from a catalog, or sample data.
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Choose a tool to analyze your data. Each of the tutorials describes a tool.
Tutorials for analyzing and visualizing data
Each of these tutorials provides a description of the tool, a video, the instructions, and additional learning resources:
| Tutorial | Description | Expertise for tutorial |
|---|---|---|
| Analyze data in a Jupyter notebook | Load data, run, and share a notebook. | Understand generated Python code. |
| Refine and visualize data with Data Refinery | Prepare and visualize tabular data with a graphical flow editor. | Select operations to manipulate data. |
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 on the platform.
The model workflow has three main steps: build a model asset, deploy the model, and build trust in the model.
Tutorials for building, deploying, and trusting models
Each 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 that uses Python code. |
| Build and deploy a machine learning model with SPSS Modeler | Build a C5.0 model that uses 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. |
| Automate the lifecycle for a model with pipelines | Create and run a pipeline to automate building and deploying a machine learning model. | Drop operation nodes on a canvas and select properties. |
Other learning resources
Guided tutorials
Access the Build an AI model sample project to follow a guided tutorial in the Samples. After you create the sample project, the readme provides instructions:
- Choose Explore and prepare data to remove anomalies in the data with Data Refinery.
- Choose Build a model in a notebook to build a model with Python code.
- Choose Build and deploy a model to automate building a model with the AutoAI tool.
Documentation
General
Preparing data
Analyzing and visualizing data
Building, deploying, and trusting models
Videos
- A comprehensive set of videos that show many common tasks in watsonx.
Samples
Find sample data sets, projects, models, prompts, and notebooks in the Samples area to gain hands-on experience:
Notebooks that you can add to your project to get started analyzing data and building models.
Projects that you can import containing notebooks, data sets, prompts, and other assets.
Data sets that you can add to your project to refine, analyze, and build models.
Prompts that you can use in the Prompt Lab to prompt a foundation model.
Foundation models that you can use in the Prompt Lab.
Training
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Watson Studio Methodology is an IBM Training e-Learning course that provides an in-depth look at Watson Studio.
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Take control of your data with Watson Studio is a learning path that consists of step-by-step tutorials that explain the process of working with data using Watson Studio.
Parent topic: Getting started