Amazon SageMaker Canvas
Generate accurate ML predictions—no code required
Leverage 160 workspace instance hours per month free
for the first 2 months with the AWS Free Tier
Use a visual point-and-click interface to generate accurate ML predictions for classification, regression, forecasting, natural language processing (NLP), and computer vision (CV).
Access ready-to-use foundation or predictive models, or automatically create custom ML models to generate output with a few clicks.
Boost collaboration between business analysts and data science teams by sharing, reviewing, and updating ML models across tools.
Import ML models from anywhere and generate predictions directly in Amazon SageMaker Canvas.
Amazon SageMaker Canvas enables business analysts and data science teams to build their own models without having to write a single line of code. You can build ML models for common use cases such as regression and forecasting. In addition, you can access and evaluate foundation models (FMs) from Amazon Bedrock or public FMs from Amazon SageMaker JumpStart for content generation, text extraction, and text summarization to support generative AI solutions.
How it works
With the Amazon SageMaker Canvas no-code interface, you can access ready-to-use FMs and predictive models, or create custom models, to extract information and generate AI output in minutes. Canvas supports FMs from Amazon Bedrock including Claude, Titan, and Jurassic, and public models from SageMaker JumpStart such as Falcon and MPT. Using these FMs in SageMaker Canvas allows you to generate, extract, and summarize content. In addition, you can analyze and classify content using ready-to-use models for sentiment analysis, object detection, or document analysis. To get started with ready-to-use models you simply select the model, upload your data, and generate model output with a single click.
You can also build your own custom model without coding for classification, regression, forecasting, text classification, or image classification. To get started with custom models, you can import data from disparate sources, select values you want to predict, automatically prepare and explore data, and create an ML model with a few clicks. Additionally, business and data scientist teams can collaborate. Models built in SageMaker Canvas can be shared with data scientists, who are using SageMaker Studio, for review and updates. Data scientists can also share ML models built anywhere, allowing you to generate AI output from those models in SageMaker Canvas without writing any code.
Ready-to-use models
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Predict
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Access
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Test
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Custom models
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Create
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Analyze
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Collaborate
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Bring your own model
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Create
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Explore
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Analyze
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Collaborate
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Bring your own model
Use cases
Generate sales and marketing content
Create personalized, engaging, and high-quality sales and marketing content such as social media posts, product descriptions, and email campaigns.
Summarize Content
Produce concise summaries of articles, blog posts, and documents to identify the most important information, highlight key takeaways, and more quickly distill information.
Detect sentiment in free-form text
Detect sentiment in lines of text, such as positive, negative, neutral, or mixed, in product reviews, customer support tickets, or documents.
Extract information from documents
Analyze and extract information from a variety of documents, such as insurance claims, invoices, expense reports, or identity documents.
Identify objects and text in images
Automatically identify objects, concepts, scenes, and lines of text in your images.
Predict customer churn
Use product consumption and purchase history data to uncover customer churn patterns and predict those at risk of churning in the future.
Plan inventory efficiently
Forecast inventory levels by combining historical sales and demand data with associated web traffic, pricing, product category, and holiday data.
Classify images
Classify images based on custom categories specific to your business, such as identifying defective products on an assembly line, categorizing products for inventory management, or performing vehicle damage assessments to accelerate insurance claims processing.
Classify text
Classify lines of text based on custom categories specific to your business, such as social media feedback, customer support tickets, or product reviews.
Customer success
Based in South Korea, Samsung Electronics is a global company offering people around the world access to technology, such as mobile phones, computers, and smart devices. The Samsung Device Solutions division of the company focuses on the inner workings of electronic devices to provide maximum performance, reliability, and longevity.
“Using Amazon SageMaker Canvas is simple, and the interface is user friendly. Even a business analyst like me can analyze data and get insights using machine learning.”
Dooyong Lee, Manager of Marketing Intelligence, Samsung Electronics
Clarium is a healthcare supply chain platform that uses machine learning and data tools to empower healthcare providers by optimizing inventory levels, reducing costs, and improving operational efficiency.
“Our clients are often hindered by the quality and integration of their own data—a challenge that is ubiquitous among hospitals across the US. In addition, accumulated decades of inconsistent quality assurance practices and fragmented data processes have resulted in the arduous task of cleansing, validating, and integrating billions of rows of erroneous data. To help them tackle this challenge, our analytics teams use Amazon SageMaker Canvas to derive, cleanse, and standardize data coming from an unlimited number of sources from healthcare providers in the US, allowing them to easily build custom text classification models—all without writing a single line of code. With SageMaker Canvas, we offer our clients validated and standardized product descriptions and classifications for ERP data, verified clinical classifications for procedural utilization data, and data-driven recommendations designed to optimize the quality of patient care, all while reducing costs and saving time. By equipping our clients with trustworthy data and cutting-edge analytics, we empower them to look ahead and build a brighter future for healthcare, instead of constantly playing catch-up with the present.”
Justin Jacobson, Head of Analytics, Clarium Health
Siemens Energy is energizing society. They are transforming in key focus areas of environmental, social, and governance (ESG), and their innovation is making the future of tomorrow different today, for both their partners and their people.
“The core of our data science strategy at Siemens Energy is to bring the power of machine learning to all business users by enabling them to experiment with different data sources and machine learning frameworks without requiring a data science expert. This enables us to increase the speed of innovation and digitalization of our energy solutions such as Dispatch Optimizer and Diagnostic services. We found Amazon SageMaker Canvas a great addition to the Siemens Energy machine learning toolkit, because it allows business users to perform experiments while also sharing and collaborating with data science teams. The collaboration is important because it helps us productionalize more ML models and ensure all models adhere to our quality standards and policies.”
Davood Naderi, Data Science Team Lead at Industrial Applications, Siemens Energy
A subsidiary of Koch Industries since 2004, INVISTA brings to market the proprietary ingredients for nylon 6,6 and recognized brands including CORDURA and ANTRON.
“Our business analysts are data savvy, and we needed the ability to let them create predictive models. Equally important, however, was to ensure that our data science team had visibility into the models built so that they can support and productionize as needed. We foresee Amazon SageMaker Canvas empowering our business users and process engineers to start working on data science problems that were previously owned by the data science team. The intuitive user interface and easy-to-navigate options of Amazon SageMaker Canvas allow business users to import a variety of data, minimize the need to manually clean up data, and apply a variety of algorithms to find the model that best fits the data with a few clicks. The code and data can easily be sent to the data science team through Amazon SageMaker Studio, allowing them to integrate models into their model management system and see a full picture of models enterprise wide.”
Caleb Wilkinson, Lead Data Scientist, INVISTA
The BMW Group, headquartered in Munich, Germany, is a global manufacturer of premium automobiles and motorcycles, covering the brands BMW, BMW Motorrad, MINI, and Rolls-Royce. It also provides premium financial and mobility services.
“The use of Artificial Intelligence as a key technology is an integral element in the process of digital transformation at the BMW Group. The company already employs AI throughout the value chain, enabling it to generate added value for customers, products, employees, and processes. In the past few years, we have industrialized many top BMW Group use cases measured by business value impact. We believe Amazon SageMaker Canvas can add a boost to our AI/ML, scaling across the BMW Group. With SageMaker Canvas, our business users can easily explore and build ML models to make accurate predictions without writing any code. SageMaker also allows our central data science team to collaborate and evaluate the models created by business users before publishing them to production.”