Introduction to AWS SageMaker and its features

Introduction to AWS SageMaker and its features

Recipe Objective - Introduction to Amazon SageMaker and its features?

The Amazon SageMaker is a widely used service and is defined as a managed service in the Amazon Web Services (AWS) cloud which provides tools to build, train and deploy machine learning (ML) models for predictive analytics applications. Amazon SageMaker platform automates the unvarying work of building the production-ready artificial intelligence (AI) pipelines. Amazon SageMaker also enables the developers to deploy Machine Learning models on embedded systems and edge devices. The Amazon SageMaker creates the fully managed Machine Learning instance in the Amazon Elastic Compute Cloud (EC2). It supports the open-source Jupyter Notebook web application which enables developers to share live code and collaborate. Amazon SageMaker runs the Jupyter computational processing notebooks. The notebooks include the drivers, packages and libraries for similar deep learning platforms and frameworks. Developers can launch the prebuilt notebook that AWS supplies for a variety of applications and use cases and they can then customize it according to the data set and schema that needs to be further trained. Developers also can use the custom-built algorithms written in one of the supported Machine Learning frameworks or some code that has been packaged as the Docker container image. Amazon SageMaker helps in pulling the data from Amazon Simple Storage Service (S3) and there is no defined practical limit to the size of the data set.

Benefits of Amazon SageMaker

The Amazon SageMaker enables more people to innovate with Machine Learning through the choice of tools—integrated development environments for data scientists, machine learning engineers and no-code visual interfaces for the business analysts thus making machine learning more accessible. Amazon SageMaker helps in accessing, labelling, and processing large amounts of structured data (tabular data) and unstructured data (photos, video, and audio) for Machine Learning thus helping in preparing data in scale. Amazon SageMaker helps in reducing the training time from hours to minutes with the optimized infrastructure thereby boosting team productivity up to 10 times with the purpose-built tools thus accelerating machine learning development. Amazon SageMaker helps in automating and standardizing MLOps practices across the organization to build, train, deploy, and manage machine learning models at a larger scale.

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System Requirements

  • Any Operating System(Mac, Windows, Linux)
  • This recipe explains Amazon SageMaker and the features of Amazon SageMaker.

Features of Amazon SageMaker

    • It provides a One-Click feature for Training the models.

Amazon SageMaker trains the model by first specifying the location of data, indicating the type of SageMaker instances, and getting started with a single click. It sets up the distributed compute cluster, performs training, outputs results to the Amazon S3 and further tears down the cluster.

    • Amazon SageMaker provides Distributed training.

Amazon SageMaker makes it faster for its users to perform the distributed training by splitting the data across multiple GPUs which achieves near-linear scaling efficiency. It also helps split the model across multiple GPUs by automatically profiling and partitioning the model with fewer than 10 lines of code.

    • Amazon SageMaker provides automatic Model Tuning of models.

Amazon SageMaker enables automatic tuning of machine learning models by adjusting thousands of combinations of algorithm parameters to arrive at the most accurate predictions model that is capable of producing further saving weeks of effort. The Automatic model tuning uses a machine learning technique to quickly tune the model.

    • Amazon SageMaker helps in Profiling and Debugging Training Runs.

Amazon SageMaker Debugger captures the metrics and profiles training jobs in real-time enabling users to correct performance problems quickly before the model is deployed to production.

    • Amazon SageMaker provides managed Spot Training.

Amazon SageMaker provides the managed Spot Training to help further reduce training costs by up to 90%. Training jobs automatically run when compute capacity becomes available and are further made resilient to interruptions caused by the changes in the capacity.

    • Amazon SageMaker supports Reinforcement Learning.

Amazon SageMaker supports reinforcement learning in addition to traditional supervised and unsupervised learning. It has built-in, fully-managed reinforcement learning algorithms including the newest and best performing in academic literature.

    • Amazon SageMaker supports and is optimized for Major Frameworks.

Amazon SageMaker is further optimized for many popular deep learning frameworks like TensorFlow, Apache MXNet, PyTorch, and many more. Frameworks supported are always up-to-date with the latest version and are further optimized for performance on AWS.

    • Amazon SageMaker supports AutoML.

Amazon SageMaker autopilot automatically builds, trains, and tunes the best machine learning models, based on the users' data while allowing them to maintain full control and visibility. The model can be deployed to the production with just one click or iterated to further improve the model quality.

Amazon SageMaker allows users to operate on a fully secure Machine Learning environment from day one. A comprehensive set of security features is available for use to help further support a broad range of industry regulations.

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