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. SageMaker Canvas makes it easy to browse and access disparate cloud and on-premises data sources. Once data is uploaded, you can quickly combine datasets, cleanse data, and automatically apply a variety of data adjustments. SageMaker Canvas then makes it easy to build ML models and generate accurate predictions with just a few clicks. You can also easily publish results, explain and interpret models, and share models with others within your organization.
Browse, import, and join data
You can browse and import data using the SageMaker Canvas visual, point-and-click user interface. SageMaker Canvas supports CSV file types, and discovers AWS data sources that your account has access to, including Amazon Simple Storage Service (S3) and Amazon Redshift. You can also drag and drop files from your local disk, and use pre-built connectors to import data from third-party sources such as Snowflake. In addition, you can use the join operation to join data across multiple sources and create new unified datasets for training prediction models. For example, you can join transactional data in Amazon Redshift that contains customer IDs with CSV tables in Amazon S3 that contain customer profile data to create a new dataset. In the SageMaker Canvas visual interface, you can verify data was imported correctly, understand the mean and median, and determine if there are missing values in your data. You can also profile data and identify correlations between columns in your dataset.
Built-in data cleansing and data adjustments
SageMaker Canvas helps minimize the need to manually clean data by automatically detecting errors in your data and cleaning them. SageMaker Canvas automatically cleans and prepares your data, such as detecting and correcting missing values, anomalous values, and duplicate rows and columns.
SageMaker Canvas uses a subset of your data to build a model quickly to check if your data is ready to generate an accurate prediction. Using this sample model, you can understand the current model accuracy and the relative impact of each column on predictions.
Automatic prediction creation
Once you connect to your data sources, select a dataset, and prepare your data, you can select the feature that you want to predict and initiate the model creation job. SageMaker Canvas will automatically identify the problem type, generate new relevant features, test hundreds of prediction models using ML techniques such as linear regression, logistic regression, deep learning, time series forecasting, and gradient boosting, and build the model that makes the most accurate predictions based on your dataset.
SageMaker Canvas makes it easy to share work with others. You can share your SageMaker Canvas models and datasets with data scientists who use SageMaker Studio. This makes it easy for them to review and approve models and datasets created by business analysts.
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Get started building with Amazon SageMaker Canvas in the AWS Management Console.