0 / 0
Quick start tutorials

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.

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:

  1. Create a project.

  2. If necessary, create the service instance that provides the tool you want to use and associate it with the project.

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

  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. If necessary, create the service instance that provides the tool you want to use and associate it with the project.

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

  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 model workflow has three main steps: build a model asset, deploy the model, and build trust in the model.

Overview of model workflow

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.
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. If necessary, create the service instance that provides the tool you want to use and associate it with the project.

  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.
Try the watsonx.ai end-to-end use case Follow a use case from data preparation through prompt engineering. Use various tools, such as notebooks and Prompt Lab.

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. If necessary, create the service instance that provides the tool you want to use and associate it with the project.

  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. Run a notebook to configure the models and use Watson OpenScale to evaluate.

Other learning resources

Guided tutorials

Access the Build an AI model sample project to follow a guided tutorial in the Resource hub. 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.
Video disclaimer: Some minor steps and graphical elements in this video might differ from your platform.

Watch this video series to see how to work with the assets in the sample project.

General

Preparing data

Analyzing and visualizing data

Building, deploying, and trusting models

Working with generative AI

Governing AI

Videos

Samples

Find sample data sets, projects, models, prompts, and notebooks in the Resource hub area to gain hands-on experience:

Notebook icon Notebooks that you can add to your project to get started analyzing data and building models.

Project icon Projects that you can import containing notebooks, data sets, prompts, and other assets.

Data set icon Data sets that you can add to your project to refine, analyze, and build models.

Prompt icon Prompts that you can use in the Prompt Lab to prompt a foundation model.

Model icon Foundation models that you can use in the Prompt Lab.

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

Generative AI search and answer
These answers are generated by a large language model in watsonx.ai based on content from the product documentation. Learn more