Amazon SageMaker Canvas

Generate accurate machine learning predictions—no code required

Create up to 10 models with up to 1M cells of data free

for the first 2 months with the AWS Free Tier

How it works

Amazon SageMaker Canvas expands access to machine learning (ML) by providing business analysts with a visual point-and-click interface that allows them to generate accurate ML predictions on their own — without requiring any machine learning experience or having to write a single line of code.
How Amazon SageMaker Canvas works


Generate ML predictions without writing code

SageMaker Canvas provides a visual point-and-click interface for business analysts to build ML models and generate accurate predictions without writing code or having any previous ML experience.

Quickly access and prepare data for ML

With SageMaker Canvas, you can quickly connect and access data from cloud and on-premises data sources, combine datasets, and create unified datasets for training ML models. SageMaker Canvas automatically detects and corrects data errors and analyzes data readiness for ML.

Use built-in AutoML to generate predictions

SageMaker Canvas uses powerful AutoML technology from Amazon SageMaker to automatically create ML models based on your unique use case. This allows SageMaker Canvas to identify the best model based on your dataset so you can generate accurate preditions—whether singular or in bulk.

Validate ML models with data scientists

SageMaker Canvas is integrated with Amazon SageMaker Studio, making it easier for business analysts to share models and datasets with data scientists so they can validate and further refine the ML model.

Use cases

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.

Optimize price and revenue

Predict prices of goods and services using historical demand and pricing and seasonal trends to offer the best prices to customers while maximizing revenue.

Improve on-time deliveries

Predict delivery times using order, fulfillment, transit, and holiday data to optimize the supply chain and deliver goods with greater efficiency.

Plan inventory efficiently

Predict inventory needs by combining historical sales and demand data with associated web traffic, pricing, product category, weather, and holiday data.

Customer success

Siemens Energy

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

BMW Group

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." 

Marc Neumann, Product Owner - AI Platform, BMW Group

Getting started

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Amazon SageMaker Canvas