Amazon SageMaker Canvas features

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. With SageMaker Canvas, you can access ready-to-use models or create custom models to extract information and generate predictions from thousands of documents, images, and lines of text in minutes. To get started with ready-to-use models, you can simply select a ready-to-use model, upload your data, and generate predictions with a single click. You can also build your own custom model for classification, regression, forecasting, text classification, or image classification. You can import disparate cloud and on-premises data sources, analyze, explore, and visualize the relationships between features, and even create new features using functions and operators. SageMaker Canvas then lets you create ML models and generate accurate predictions with a few clicks. You can also publish results, explain, and interpret models. In addition, you can collaborate with data scientists within your organization. You can share models for review and update, and data scientists can share ML models built in other tools, so you can generate predictions on those models directly inside SageMaker Canvas.

To quickly generate ML predictions, you can access ready-to-use models for a variety of natural language processing and computer vision use cases. Ready-to-use models include sentiment analysis, object detection in images, text detection in images, entities extraction, language detection, personal information detection, expense analysis, identify document analysis, and document analysis. Additionally, ready-to-use models do not require model building, and are powered by AWS AI services, including Amazon Rekognition, Amazon Textract, and Amazon Comprehend. To get started, you can bring your data, such as text, images, or documents, and select a ready-to-use model to generate predictions with a single click.

With Amazon SageMaker Canvas, you can also create custom classification, regression, forecasting, text classification, or image classification models that are trained using your own data. SageMaker Canvas provides a visual, point-and-click interface to connect, prepare, analyze, and explore data for creating ML models and generating predictions.

You can browse and import data using the SageMaker Canvas visual interface. SageMaker Canvas supports CSV, JPEG, and PNG file types and discovers AWS data sources that your account has access to, including Amazon Simple Storage Service (S3), Amazon Athena (Glue Data Catalog), and Amazon Redshift. You can also drag and drop files from your local disk or import data from over 40 third-party data sources, such as SAP OData, Salesforce, and 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 Salesforce that contains customer IDs with CSV tables in Amazon S3 that contain customer profile data to create a new dataset. You can then verify that data was imported correctly, understand the data distribution with parameters such as 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.

SageMaker Canvas offers exploratory data analysis (EDA), allowing you to prepare, explore, and analyze your data. You can impute missing values and replace outliers with custom values, visualize the relationships between features, and create new features using functions and operators.

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 identify the problem type, generate new relevant features, test hundreds of prediction models based on the problem type (using ML techniques such as linear regression, logistic regression, deep learning, time-series forecasting, and gradient boosting), and create the model that makes the most accurate predictions based on your dataset.

SageMaker Canvas makes it easier to collaborate with data scientists. You can share your SageMaker Canvas models with data scientists who use SageMaker Studio. They can review, update, and share updated models with you, so you can analyze and generate predictions on updated models in SageMaker Canvas.

Data scientists can share any ML model built in other tools, once it is registered in the SageMaker Model Registry, so you can generate predictions on these models in SageMaker Canvas. Data scientists can also share SageMaker Autopilot models and trained models from SageMaker JumpStart so you can generate highly accurate predictions on models built by data scientists.