Machine learning (ML) can help companies make better business decisions through advanced analytics. Companies across industries apply ML to use cases such as predicting customer churn, demand forecasting, credit scoring, predicting late shipments, and improving manufacturing quality.
In this blog post, we’ll look at how Amazon SageMaker Canvas delivers faster and more accurate model training times enabling iterative prototyping and experimentation, which in turn speeds up the time it takes to generate better predictions.
Training machine learning models
SageMaker Canvas offers two methods to train ML models without writing code: Quick build and Standard build. Both methods deliver a fully trained ML model including column impact for tabular data, with Quick build focusing on speed and experimentation, while Standard build providing the highest levels of accuracy.
With both methods, SageMaker Canvas pre-processes the data, chooses the right algorithm, explores and optimizes the hyperparameter space, and generates the model. This process is abstracted

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