Quick start tutorials

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.

Looking for videos? Visit the video library to see many common tasks in watsonx.ai and watsonx.governance.

After completing these tutorials, see the Other learning resources section to continue your learning.

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 ).

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:

  1. Create a project.

  2. Verify that a service instance that provides the tool you want to use is installed and enabled.

  3. Add data to your project. You can add data files from your local system or data from a remote data source that you connect to.

  4. Choose a tool to analyze your data. Each of the tutorials describes a tool.

  5. 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.
Generate synthetic tabular data Generate synthetic tabular data using a graphical flow editor. Select operations to generate 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:

  1. Create a project.

  2. Verify that a service instance that provides the tool you want to use is installed and enabled.

  3. Add data to your project. You can add data files from your local system, or data from a remote data source that you connect to.

  4. 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 different stages involved in the AI lifecycle are as follows:

  1. Define scope: Start by defining the scope of your project by identifying the key objectives and requirements.
  2. Prepare data: Collect and prepare data for use with machine learning algorithms.
  3. Build model: Develop and refine the AI model to solve the defined problem by training the model with prepared data.
  4. Deploy model: Deploy the model to production after the building process is complete.
  5. Automate pipeline: Automate the path to production by automating parts of the AI lifecycle.
  6. Monitor performance: Evaluate your model's performance for fairness, quality, drift and explainability.

The following diagram shows the stages of the AI lifecycle:

Stages of AI lifecycle

Your workflow to build, deploy, and trust models has these basic steps:

  1. Create a project.

  2. Verify that a service instance that provides the tool you want to use is installed and enabled.

  3. Choose a tool to build, deploy, and trust models. Each of the tutorials describes a tool.

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.
Evaluate a machine learning model Deploy a model, configure monitors for the deployed model, and evaluate the model. Run a notebook to configure the models and use Watson OpenScale to evaluate.

Working with generative AI

To get started with working with generative AI, understand the overall workflow, choose a tutorial, and check out other learning resources for working on the platform.

Your prompt engineering workflow has these basic steps:

  1. Create a project.

  2. Verify that a service instance that provides the tool you want to use is installed and enabled.

  3. Choose a tool to prompt foundation models. Each of the tutorials describes a tool.

  4. Save and share your best prompts.

Tutorials for working with generative AI

Each tutorial provides a description of the tool, a video, the instructions, and additional learning resources:

Tutorial Description Expertise for tutorial
Prompt a foundation model using Prompt Lab Experiment with prompting different foundation models, explore sample prompts, and save and share your best prompts. Prompt a model using Prompt Lab without coding.
Prompt a foundation model with the retrieval-augmented generation pattern Prompt a foundation model by leveraging information in a knowledge base. Use the retrieval-augmented generation pattern in a Jupyter notebook that uses Python code.
Tune a foundation model Tune a foundation model to enhance model performance. Use the Tuning Studio to tune a model without coding.
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.

Governing AI

To get started with governing AI, understand the overall workflow, choose a tutorial, and check out other learning resources for working on the platform.

Your AI governance workflow has these basic steps:

  1. Create a project.

  2. Verify that a service instance that provides the tool you want to use is installed and enabled.

  3. Choose a tool to govern AI. Each of the tutorials describes a tool.

Tutorials for governing AI

Each tutorial provides a description of the tool, a video, the instructions, and additional learning resources:

Tutorial Description Expertise for tutorial
Evaluate and track a prompt template Evaluate a prompt template to measure the performance of foundation model and track the prompt template through its lifecycle. Use the evaluation tool and an AI use case to track the prompt template.
Evaluate a machine learning model Deploy a model, configure monitors for the deployed model, and evaluate the model Watson OpenScale. Run a notebook to configure the monitors and use Watson OpenScale to evaluate.
Evaluate a deployment in spaces Deploy a model, configure monitors for the deployed model, and evaluate the model in a deployment space. Configure the monitors and evaluate a model in a deployment space.

Other learning resources

General

Preparing data

Analyzing and visualizing data

Building, deploying, and trusting models

Working with generative AI

Governing AI

Videos

Use case samples

Try out different use cases on a self-service site. Select a use case to experience a live application built with watsonx. Developers, access prompt selection and construction guidance, along with sample application code, to accelerate your project.

Parent topic: Getting started