Amazon SageMaker Canvas
Generate accurate ML 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
Visual point-and-click interface to connect, prepare, analyze, and explore data for building ML models and generating accurate predictions.
Automatically build ML models to run what-if analysis and generate single or bulk predictions with a few clicks.
Boost collaboration between business analysts and data scientists 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 expands access to machine learning (ML) by providing business analysts with a visual interface that allows them to generate accurate ML predictions on their own—without requiring any ML experience or having to write a single line of code.
How it works
With Amazon SageMaker Canvas, you can import data from disparate sources, select values you want to predict, automatically prepare and explore data, and quickly and more easily build ML models. You can then analyze models and generate accurate predictions with a few clicks. Additionally, you can collaborate with data scientists in two ways. First, models built in SageMaker Canvas can be shared with data scientists using SageMaker Studio for review and update. You can then analyze and generate predictions on updated models in SageMaker Canvas. Second, data scientists can share ML models built anywhere, allowing you to generate predictions on those models in SageMaker Canvas without writing any code.
Bring your own model
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.
Predict product quality
Use manufacturing line and historical data to explain and predict end-of-line product quality before products are shipped to customers.
Predict unplanned maintenance
Explain and predict unplanned maintenance events before they impact operations, using historical maintenance and operating data.
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.”