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Amazon SageMaker Studio is a fully integrated IDE unifying the tools needed for ML development. With Studio you can write code, track experiments, visualize data, and perform debugging and monitoring in a Jupyterlab-based interface.
Amazon S3 Storage Types Standard: Used to store performance-sensitive data that should have a retrieval time of milliseconds. Standard Infrequent Access: Used to store infrequently accessed data. One Zone-Infrequent Access: Used for infrequently used objects that need lower durability.
Amazon Simple Storage Service (Amazon S3) is an object storage service that offers industry-leading scalability, data availability, security, and performance. You can use Amazon S3 to store and retrieve any amount of data at any time, from anywhere.
Amazon Simple Storage Service (S3) is a storage that can be maintained and accessed over the Internet. Amazon S3 provides a web service that can be used to store and retrieve an unlimited amount of data. It is global by default and buckets are region specified. S3 in Amazon has two entities called buckets and objects.
Amazon Simple Storage Service (Amazon S3) is an object storage service that offers industry-leading scalability, data availability, security, and performance. You can use Amazon S3 to store and retrieve any amount of data at any time, from anywhere.

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Flexibility: S3 is ideal for a wide range of uses like data storage, data backup, software delivery, data archiving, disaster recovery, website hosting, mobile applications, IoT devices, and much more.
Flexibility: S3 is ideal for a wide range of uses like data storage, data backup, software delivery, data archiving, disaster recovery, website hosting, mobile applications, IoT devices, and much more.
SageMaker is a fully managed service that enables you to quickly and easily integrate machine learning-based models into your applications. This section provides an overview of machine learning and explains how SageMaker works.
Amazon S3 is object storage built to store and retrieve any amount of data from anywhere. It's a simple storage service that offers industry leading durability, availability, performance, security, and virtually unlimited scalability at very low costs.
Use Machine Learning Frameworks, Python, and R with Amazon SageMaker. You can use Python and R natively in Amazon SageMaker notebook kernels. There are also kernels that support specific frameworks. A very popular way to get started with SageMaker is to use the Amazon SageMaker Python SDK .

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