Q: What is Amazon SageMaker Canvas?
Amazon SageMaker Canvas is a visual, point-and-click service that allows business analysts to generate accurate machine learning (ML) predictions without writing any code or requiring ML expertise. SageMaker Canvas makes it easy to access and combine data from a variety of sources, automatically clean data and apply a variety of data adjustments, and build ML models to generate accurate predictions with a single click. You can also easily publish results, explain and interpret models, and share models with others within your organization to review.
Q: What data sources does Amazon SageMaker Canvas support?
SageMaker Canvas enables you to seamlessly discover AWS data sources that your account has access to, including Amazon Simple Storage Service (S3) and Amazon Redshift. You can browse and import data using the SageMaker Canvas visual, point-and-click interface. Additionally, you can drag and drop files from your local disk, and use pre-built connectors to import data from third-party sources such as Snowflake.
Q: How can I validate my data to confirm it is ready to build a model?
Amazon SageMaker Canvas provides an analyze data feature to quickly check if your data is ready for machine learning. SageMaker Canvas uses a subset of your data to build a model quickly. If the model is successfully built, you can build an accurate model with your full dataset. The analyze data feature also provides an estimated model score and column impact analysis that shows you the relative impact of each column on predictions and cross-correlations between your columns. If a column in your dataset has low impact on predictions, you may choose to exclude that column before building an ML model.
Q: How do I build an ML model to generate accurate predictions?
Once you have connected to data sources, selected a dataset, and prepared your data, you can select the target column that you want to predict to initiate a model creation job. Amazon SageMaker Canvas will automatically identify the problem type, generate new relevant features, test a comprehensive set 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 accurate predictions based on your dataset.
Q: How long does it take to build a model? How can I monitor progress during model creation?
The time it takes to build a model depends on the size of your dataset and selected build mode. Small datasets can take less than 5 minutes, and large datasets can take a few hours. As the model building progresses, Amazon SageMaker Canvas provides updates and estimated time to completion.
Q: How do I make predictions?
To make a single prediction, go to the “single prediction” tab, input values, and Amazon SageMaker Canvas will show you the prediction. You can also use sliders and pull-down menus to change input values to see the impact on the prediction. To make predictions for multiple observations or rows of data, go to the “bulk prediction” tab, drag and drop the CSV file containing your observation, and SageMaker Canvas will create a new CSV file with predictions.
Q: Can I use my Amazon SageMaker Canvas model to make predictions from an application outside SageMaker Canvas?
Amazon SageMaker Canvas provides reference code that can be used in applications to make predictions. SageMaker Canvas provides reference code for Python, curl, Java, and Node.js.
Q: How can I explain my model to others?
Amazon SageMaker Canvas provides column impact analysis which explains the impact that each column in your dataset has on a model. SageMaker Canvas also provides additional metrics that provide visibility into model performance. Additionally, when you generate predictions, you can see the column impact that identifies which columns have the most impact on each prediction.
Q: In what regions is Amazon SageMaker Canvas available?
Amazon SageMaker Canvas is available in the US East (Ohio), US East (N. Virginia), US West (Oregon), Europe (Frankfurt), Europe (Ireland), and Asia Pacific (Tokyo) AWS Regions.
Q: How do I get started with Amazon SageMaker Canvas?
You can access Amazon SageMaker Canvas through the AWS Management console by creating a SageMaker Domain and launching SageMaker Canvas. After setting up SageMaker Canvas in the console you or your IT administrator can also configure single sign-on to enable users to launch SageMaker Canvas without accessing the console.
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