Amazon SageMaker Canvas is a service that allows business analysts and citizen data scientists to use pre-built machine learning models or build their own without writing code. It supports various use cases such as sentiment analysis, document processing, and demand forecasting. The service now includes foundation models, which can generate and summarize content using generative AI. Users can access and compare different models, and the data stays within the secure AWS environment. A step-by-step guide is provided for using the generative AI capabilities of SageMaker Canvas in a customer support use case.
Introducing Amazon SageMaker Canvas: No-Code Generative AI Capabilities
Amazon SageMaker Canvas is a powerful tool that allows business analysts and citizen data scientists to leverage machine learning (ML) models without writing any code. With ready-to-use models and the ability to build custom models, you can generate accurate predictions and derive immediate insights from various types of data.
Ready-to-Use Models
SageMaker Canvas provides a range of ready-to-use models that enable you to analyze text, images, and documents. These models can perform tasks such as sentiment analysis, document processing, and object detection. They allow you to quickly gain insights and make data-driven decisions.
Custom Models
In addition to ready-to-use models, SageMaker Canvas lets you build custom ML models for specific use cases. Whether it’s demand forecasting, customer churn prediction, or defect detection in manufacturing, you can create predictive models tailored to your business needs.
Generative AI with Foundation Models
We’re excited to announce that SageMaker Canvas now supports foundation models (FMs), which enable you to use generative AI to generate and summarize content. With natural language and a conversational chat interface, you can create narratives, reports, blog posts, answer questions, summarize notes, and explain concepts effortlessly.
Secure and Private
Your data remains secure and private when using SageMaker Canvas. It is not used to improve the base models or shared with third-party providers. All data stays within your secure AWS environment.
Access to a Variety of Models
SageMaker Canvas provides access to a variety of foundation models, including Amazon Bedrock models and publicly available SageMaker JumpStart models. You can use a single model or compare multiple models side by side to find the best fit for your use case.
Prerequisites
Before using SageMaker Canvas, you need to create an AWS account and set up the necessary configurations. You may also need to request service quota increases for specific instances. These prerequisites ensure a smooth experience with SageMaker Canvas.
Handling Customer Complaints
As a customer support analyst, you can use SageMaker Canvas to analyze customer complaints and generate personalized responses. Simply follow the step-by-step process outlined in the post to retrieve sentiment, ask questions, and generate a response without writing code.
Comparing Model Responses
SageMaker Canvas allows you to compare model responses from multiple models. This feature helps you evaluate and find the best model for your specific use case. Follow the instructions provided to compare responses from different Amazon Bedrock and SageMaker JumpStart models.
Clean Up
To save costs, you can shut down SageMaker JumpStart models started from SageMaker Canvas after two hours of inactivity. This step-by-step guide explains how to shut down the models and release the resources used by the workspace instance.
Conclusion
SageMaker Canvas empowers middle managers to harness the power of AI without writing code. With its no-code approach, you can generate text, analyze data, and make informed decisions. Try out the new generative AI capabilities in SageMaker Canvas today and discover how AI can redefine your way of work.
For more information and AI solutions tailored to middle managers, reach out to us at hello@itinai.com. Stay updated on AI insights by following us on Telegram t.me/itinainews or Twitter @itinaicom.
About the Authors
Anand Iyer, Gavin Satur, Gunjan Jain, and Harpreet Dhanoa are experienced AWS Solutions Architects with expertise in various industries. They are passionate about helping businesses leverage AI and cloud technologies to drive innovation and success.
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