Amazon Q Developer is available in Amazon SageMaker Canvas in preview and is subject to change. We do not recommend using this feature in production environments. |
While using Amazon SageMaker Canvas, you can chat with Amazon Q Developer in natural language to leverage generative AI and solve problems. Q Developer is an assistant that helps you translate your goals into machine learning (ML) tasks and describes each step of the ML workflow. Q Developer helps Canvas users reduce the amount of time, effort, and data science expertise required to leverage ML and make data-driven decisions for their organizations.
Through a conversation with Q Developer, you can initiate actions in Canvas such as preparing data, building an ML model, making predictions, and deploying a model. Q Developer makes suggestions for next steps and provides you with context as you complete each step. It also informs you of results; for example, Canvas can transform your dataset according to best practices, and Q Developer can list the transforms that were used and why.
Amazon Q Developer is available in SageMaker Canvas at no additional cost to both Amazon Q Developer Pro Tier and
Free Tier users. However, standard charges apply for resources such as the SageMaker Canvas workspace
instance and any resources used for building or deploying models. For more information about
pricing, see Amazon SageMaker Canvas
pricing
Use of Amazon Q is licensed to you under MIT's 0
License
How it works
Amazon Q Developer is a generative AI powered assistant available in SageMaker Canvas that you can query using natural language. Q Developer makes suggestions for each step of the machine learning workflow, explaining concepts and providing you with options and more details as needed. You can use Q Developer for help with regression, binary classification, and multi-class classification use cases.
For example, to predict customer churn, upload a dataset of historical customer churn information to Canvas through Q Developer. Q Developer suggests an appropriate ML model type and steps to fix dataset issues, build a model, and make predictions.
Important
Amazon Q Developer is intended for conversations about machine learning problems within SageMaker Canvas. It guides users through Canvas actions and optionally answers questions about AWS services. Q Developer processes model inputs only in English. For more information about how you can use Q Developer, see Amazon Q Developer features in the Amazon Q Developer User Guide.
Supported regions
Amazon Q Developer is available within SageMaker Canvas in the following AWS Regions:
US East (N. Virginia)
US West (Oregon)
Asia Pacific (Seoul)
Asia Pacific (Tokyo)
Europe (Frankfurt)
Europe (Paris)
Amazon Q Developer capabilities available in Canvas
The following list summarizes the Canvas tasks with which Q Developer can provide assistance:
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Describe your objective – Q Developer can suggest an ML model type and general approach to solve your problem.
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Import datasets and fix issues – Tell Q Developer where your dataset is stored or upload a file to save it as a Canvas dataset. Prompt Q Developer to identify any issues in your dataset, such as outliers or missing values. Q Developer provides summary statistics about your dataset and lists any identified issues.
Then, prompt Q Developer to use Canvas's data transformation capabilities to create a revised version of your dataset. Canvas creates a Data Wrangler data flow and applies transforms according to data science best practices. For more information, see Data preparation.
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Train a model – Q Developer can tell you the Canvas recommended ML model type for your problem and a proposed model building configuration. You can use the suggested default settings or modify the configuration. When ready, prompt Q Developer to build your Canvas model.
Canvas does a Standard build by default. For more information, see How custom models work.
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Evaluate model accuracy – After building a model, Q Developer provides a summary of how the model scores across various metrics. These metrics help you determine the usefulness and accuracy of your model. Q Developer can explain any concept or metric in detail.
To view full details and visualizations, open the model from the chat or the My Models page of Canvas. For more information, see Model evaluation.
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Get predictions for new data – You can upload a new dataset and prompt Q Developer to help you open the prediction feature of Canvas.
Q Developer opens a new window in the application where you can either make a single prediction or make batch predictions with a new dataset. For more information, see Predictions with custom models.
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Deploy a model – To deploy your model for production, ask Q Developer to help you deploy your model through Canvas. Q Developer opens a new window in which you can configure your deployment.
After deploying, view your deployment details either 1) on the My Models page of Canvas in the model's Deploy tab, or 2) on the ML Ops page in the Deployments tab. For more information, see Deploy your models to an endpoint.
Prerequisites
To use Amazon Q Developer to build ML models in SageMaker Canvas, complete the following prerequisites:
Set up a Canvas application
Make sure that you have a Canvas application set up. For information about how to set up a Canvas application, see Getting started with using Amazon SageMaker Canvas.
Grant Q Developer permissions
To access Q Developer while using Canvas, you must attach the necessary permissions to the AWS IAM role used for your SageMaker AI domain or user profile. You can do this through the console or by manually attaching an AWS managed policy.
Permissions attached at the domain level apply to all user profiles in the domain, unless individual permissions are granted or revoked at the user profile level.
You can grant permissions by editing the SageMaker AI domain or user profile settings.
To grant permissions through the domain settings in the SageMaker AI console, do the following:
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Open the Amazon SageMaker AI console at https://console.aws.amazon.com/sagemaker/
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On the left navigation pane, choose Admin configurations.
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Under Admin configurations, choose Domains.
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From the list of domains, select your domain.
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On the Domain details page, select the App configurations tab.
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In the Canvas section, choose Edit.
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On the Edit Canvas settings page, go to the Amazon Q Developer section and do the following:
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Turn on Enable Amazon Q Developer in SageMaker Canvas for natural language ML to add the permissions to chat with Q Developer in Canvas to your domain's execution role.
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(Optional) Turn on Enable Amazon Q Developer chat for general AWS questions if you want to ask Q Developer questions about various AWS services (for example: Describe how Athena works).
Note
When making general AWS queries to Q Developer, your requests route through the US East (N. Virginia) AWS Region. To prevent your data from routing through US East (N. Virginia), turn off the Enable Amazon Q Developer chat for general AWS questions toggle.
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(Optional) Configure access to Q Developer from your VPC
If you have a VPC that is configured without public internet access, you can add a VPC endpoint for Q Developer. For more information, see Configure Amazon SageMaker Canvas in a VPC without internet access.
Getting started
To use Amazon Q Developer to build ML models in SageMaker Canvas, do the following:
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Open your SageMaker Canvas application.
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In the left navigation pane, choose Amazon Q.
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Choose Start a new conversation to open a new chat.
When you start a new chat, Q Developer prompts you to state your problem or provide a dataset.

Q Developer tracks any Canvas artifacts you import or create during the conversation, such as transformed datasets and models. You can access them from the chat or other Canvas application tabs. For example, if Q Developer fixes issues in your dataset, you can access the new, transformed dataset from the following places:
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The artifacts sidebar in the Q Developer chat interface
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The Datasets page of Canvas, where you can view both your original and transformed datasets
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The Data Wrangler page of Canvas, where Q Developer creates a new data flow for your dataset
The following screenshot shows the original dataset and the transformed dataset in the sidebar of a chat.

When your data is ready, ask Q Developer to help build a Canvas model. The following screenshot shows how you can prompt Q Developer to initiate a Canvas model build with only a few prompts.

After building your model, you can perform additional actions using either natural language in the chat or the artifacts sidebar menu. For example, you can view model details and metrics, make predictions, or deploy the model. The following screenshot shows the sidebar where you can choose these additional options.

You can also perform any of these actions by going to the My Models page of Canvas and selecting your model. From your model's page, you can navigate to the Analyze, Predict, and Deploy tabs to view model metrics and visualizations, make predictions, and manage deployments, respectively.