Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. Sagemaker provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don’t have to manage servers. It also provides common ML algorithms that are optimized to run efficiently against extremely large data in a distributed environment.
SageMaker requires that the training data for an ML model be present either in Amazon Simple Storage Service (Amazon S3), Amazon Elastic File System (Amazon EFS) or Amazon FSx for Lustre (for more information, refer to Access Training Data). In order to train a model using data stored outside of the three supported storage services, the data first needs to be ingested into one of these services (typically Amazon

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