Launched in 2021, Amazon SageMaker Canvas is a visual, point-and-click service that allows business analysts and citizen data scientists to use ready-to-use machine learning (ML) models and build custom ML models to generate accurate predictions without the need to write any code. Ready-to-use models enable you to derive immediate insights from text, image, and document data (such as sentiment analysis, document processing, or object detection in images). Custom models allow you to build predictive models for use cases such as demand forecasting, customer churn, and defect detection in manufacturing.
We are excited to announce that SageMaker Canvas is expanding its support of ready-to-use models to include foundation models (FMs), enabling you to use generative AI to generate and summarize content. You can use natural language with a conversational chat interface to perform tasks such as creating narratives, reports, and blog posts; answering questions; summarizing notes and articles; and explaining concepts, without writing a single line of code. Your data is not used to improve the base models, is not shared with third-party model providers, and stays entirely within your secure AWS environment.
SageMaker Canvas allows you to access a variety of FMs that include Amazon Bedrock models (such as Claude 2 from Anthropic and Jurassic-2 from AI21 Labs) and publicly available Amazon SageMaker JumpStart models, including Falcon-7B-Instruct, Falcon-40B-Instruct, and MPT-7B-Instruct). You may use a single model or up to three models to compare model responses side by side. In SageMaker Canvas, Amazon Bedrock models are always active, allowing you to use them instantly. SageMaker JumpStart models can be started and deployed in your AWS account on demand and are automatically shut down after two hours of inactivity.
Let’s explore how to use the generative AI capabilities of SageMaker Canvas. For this post, we work with a fictitious enterprise customer support use case as an example.
Prerequisites
Complete the following prerequisite steps:
Create an AWS account.
Set up SageMaker Canvas and optionally configure it to use a VPC without internet access.
Set up model access in Amazon Bedrock.
Request service quota increases for g5.12xlarge and g5.2xlarge, if required, in your Region. These instances are required to host the SageMaker JumpStart model endpoints. Other instances may be selected based on availability.
Handling customer complaints
Let’s say that you’re a customer support analyst who handles complaints for a bicycle company. When receiving a customer complaint, you can use SageMaker Canvas to analyze the complaint and generate a personalized response to the customer. To do so, complete the following steps:
On the SageMaker console, choose Canvas in the navigation pane.
Choose your domain and user profile and choose Open Canvas to open the SageMaker Canvas application.
SageMaker Canvas is also accessible using single sign-on or other existing identity providers (IdPs) without having to first access the SageMaker console.
Choose Generate, extract and summarize content to open the chat console.
With the Claude 2 model selected, enter your instructions to retrieve the customer sentiment for the provided complaint and press Enter.
You may want to know the specific problems with the bicycle, especially if it’s a long complaint. So, ask for the problems with the bicycle. Note that you don’t have to repost the complaint because SageMaker Canvas stores the context for your chat.
Now that we understand the customer’s problem, you can send them a response including a link to the company’s feedback form.
In the input window, request a response to the customer complaint.
If you want to generate another response from the FM, choose the refresh icon in the response section.
The original response and all new responses are paginated within the response section. Note that the new response is different from the original response. You can choose the copy icon in the response section to copy the response to an email or document, as required.
You can also modify the model’s response by requesting specific changes. For example, let’s ask the model to add a $50 gift card offer to the email response.
Comparing model responses
You can compare the model responses from multiple models (up to three). Let’s compare two Amazon Bedrock models (Claude 2 and Jurassic-2 Ultra) with a SageMaker JumpStart model (Falcon-7B-Instruct) to evaluate and find the best model for your use case:
Choose New chat to open a chat interface.
On the model drop-down menu, choose Start up another model.
On the Foundation models page, under Amazon SageMaker JumpStart models, choose Falcon-7B-Instruct and in the right pane, choose Start up model.
The model will take around 10 minutes to start.
On the Foundation models page, confirm that the Falcon-7B-Instruct model is active before proceeding to the next step.
Choose New chat to open a chat interface.
Choose Compare to display a drop-down menu for the second model, then choose Compare again to display a drop-down menu for the third model.
Choose the Falcon-7B-Instruct model on the first drop-down menu, Claude 2 on the second drop-down menu, and Jurassic-2 Ultra on the third drop-down menu.
Enter your instructions in the chat input box and press Enter.
You will see responses from all three models.
Clean up
Any SageMaker JumpStart models started from SageMaker Canvas will be automatically shut down after 2 hours of inactivity. If you want to shut down these models sooner to save costs, follow the instructions in this section. Note that Amazon Bedrock models are not deployed in your account, so there is no need to shut these down.
To shut down the Falcon-40B-Instruct SageMaker JumpStart model, you can choose from two methods:
On the results comparison page, choose the Falcon-7B-Instruct model’s options menu (three dots), then choose Shut down model.
Alternatively, choose New chat, and on the model drop-down menu, choose Start up another model. Then, on the Foundation models page, under Amazon SageMaker JumpStart models, choose Falcon-7B-Instruct and in the right pane, choose Shut down model.
Choose Log out in the left pane to log out of the SageMaker Canvas application to stop the consumption of SageMaker Canvas workspace instance hours and release all resources used by the workspace instance.
Conclusion
In this post, you learned how to use SageMaker Canvas to generate text with ready-to-use models from Amazon Bedrock and SageMaker JumpStart. You used the Claude 2 model to analyze the sentiment of a customer complaint, ask questions, and generate a response without a single line of code. You also started a publicly available model and compared responses from three models.
For Amazon Bedrock models, you are charged based on the volume of input tokens and output tokens as per the Amazon Bedrock pricing page. Because SageMaker JumpStart models are deployed on SageMaker instances, you are charged for the duration of usage based on the instance type as per the Amazon SageMaker pricing page.
SageMaker Canvas continues to democratize AI with a no-code visual, interactive workspace that allows business analysts to build ML models that address a wide variety of use cases. Try out the new generative AI capabilities in SageMaker Canvas today! These capabilities are available in all Regions where Amazon Bedrock or SageMaker JumpStart are available.
About the Authors
Anand Iyer has been a Principal Solutions Architect at AWS since 2016. Anand has helped global healthcare, financial services, and telecommunications clients architect and implement enterprise software solutions using AWS and hybrid cloud technologies. He has an MS in Computer Science from Louisiana State University Baton Rouge, and an MBA from USC Marshall School of Business, Los Angeles. He is AWS certified in the areas of Security, Solutions Architecture, and DevOps Engineering.
Gavin Satur is a Principal Solutions Architect at Amazon Web Services. He works with enterprise customers to build strategic, well-architected solutions and is passionate about automation. Outside of work, he enjoys family time, tennis, cooking, and traveling.
Gunjan Jain is an AWS Solutions Architect in SoCal and primarily works with large financial services companies. He helps with cloud adoption, cloud optimization, and adopting best practices for being Well-Architected on the cloud.
Harpreet Dhanoa, a seasoned Senior Solutions Architect at AWS, has a strong background in designing and building scalable distributed systems. He is passionate about machine learning, observability, and analytics. He enjoys helping large-scale customers build their cloud enterprise strategy and transform their business in AWS. In his free time, Harpreet enjoys playing basketball with his two sons and spending time with his family.
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