From the course: Machine Learning with SageMaker by Pearson

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Overview of SageMaker built-in algorithms and JumpStart models

Overview of SageMaker built-in algorithms and JumpStart models - Amazon SageMaker Tutorial

From the course: Machine Learning with SageMaker by Pearson

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Overview of SageMaker built-in algorithms and JumpStart models

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When you go into SageMaker and you create a domain and then you log into SageMaker, you can start a tool called Canvas. Canvas allows you to very quickly create machine learning models, feed it data, and receive predictions. Canvas is a no-code or low-code option for getting a machine learning model created. As a result, being low-code, it provides pre-packaged algorithms for your use in SageMaker. So we can see here in the screenshot here, XGBoost, linear models, extra trees, random forest. These are available to you, so you don't have to try to implement them yourself. Optimized for large-scale distributed training, cover regression, classification, clustering, and more. And you don't need to implement custom code. These are simply there for your use. Key built-in algorithms, we have XGBoost, linear learner, k-means, and sequence-to-sequence. We can see here on this slide, XGBoost is preferred algorithm for classification and regression, linear learner for supervised learning for…

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