Getting started using Q chat - Amazon SageMaker Unified Studio
Example 1Example 2

Getting started using Q chat

Use Q chat as follows. Make sure you are signed in with an ID that is configured for Q chat access.

  1. Log in to your AWS account and navigate to the access portal, such as with your SSO login.

    Open the SageMaker Unified Studio console through the access portal, and then navigate to your project.

  2. Open a Jupyter notebook by choosing Build, and then choosing JupyterLab. A Jupyter notebook cell page opens.

  3. Choose the icon on the left for Q chat with Amazon Q Developer. If this is the first time, a message displays for you to acknowledge the AWS policies for responsible AI.

    An image of the Q chat icon.

  4. Keep the toggle for Agentic coding ON.

  5. Type questions to interact with Q chat. Type over the Ask a question... line.

You can get started using Q chat with the following examples.

Example 1: Ask for information about your project

This example shows how Q chat can provide context aware responses for your project resources.

  1. To open JupyterLab, choose Build, and then choose JupyterLab. If you are in JupyterLab, you can chat with Q with additional Amazon Q chat contextual awareness.

  2. In the Q chat field, enter the following.

    Can you tell me about my project?

    The response returns where Q asks follow-up questions and shows your files.

Example 2: Create and run a data pipeline

This example shows how Q chat can perform complex tasks for you, such as creating and running a data pipeline in your project.

  1. To open JupyterLab, choose Build, and then choose JupyterLab. If you are in JupyterLab, you can chat with Q with additional Amazon Q chat contextual awareness.

  2. In the Q chat field, enter the following.

    Can you help me set up and run a data pipeline?

    The following diagram shows the response.

    An example response.

    The following image shows Q asking questions and explaining the task.

    An example response.

    The following image shows Q creating the shell file for you in your workspace.

    An example response.

    The following image shows Q creating the files and describing them.

    An example response.

    The following image shows Q providing the instructions to run the pipeline.

    An example response.

    The following image shows the notebook file that Q created for you in your workspace.

    An example response.
  3. Get access to data

    Before visualizing data, you might need to request access to the data by subscribing to data in Amazon SageMaker Catalog.

  4. Create new connections

    You can create connections directly to Amazon Redshift and other third party sources like Oracle and Snowflake from Amazon SageMaker Unified Studio. You configure connection details and credentials securely, and you can manage them within the project. For detailed steps, see Amazon Redshift compute connections and Data connections in Amazon SageMaker Lakehouse.