Explain the Amazon SageMaker and advantages of SageMaker

This recipe explains what the Amazon SageMaker and advantages of SageMaker

Recipe Objective - Explain the Amazon SageMaker and advantages of SageMaker?

The Amazon SageMaker is a widely used cloud machine-learning platform and is defined as a platform that enables developers to create, train, and deploy machine learning (ML) models in the cloud launched in November 2017. The Amazon Web Services SageMaker also enables developers to deploy the Machine Learning models on embedded systems and edge devices. The Amazon Web Services SageMaker enables developers to operate at several levels of abstraction when training and deploying machine learning models. The AWS SageMaker provides the pre-trained ML models which can be deployed as-is at its highest level of abstraction. Further, AWS SageMaker provides several built-in ML algorithms which developers can train on their data. Further, SageMaker provides managed instances of TensorFlow and Apache MXNet in which developers can create their Machine Learning algorithms from the scratch. A developer can connect the SageMaker-enabled Machine Learning models to other AWS services regardless of which level of abstraction is used, such as the Amazon DynamoDB database for the structured data storage, AWS Batch for the offline batch processing or the Amazon Kinesis for the real-time processing. Amazon SageMaker has several interfaces for the developers to interact with it. Firstly, there is a web API that remotely controls the SageMaker server instance while the web API is agnostic to the programming language used by the developer, Amazon provides SageMaker API bindings for several languages, including Python, JavaScript, Ruby, Java, and Go. Further, the AWS SageMaker provides managed Jupyter Notebook instances for interactively programming SageMaker and for other applications.

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Amazon SageMaker Studio

  • The Amazon SageMaker Studio provides a single, web-based visual interface where all Machine Learning development steps can be performed which improves data science team productivity by up to 10x. The Amazon SageMaker Studio gives users complete access, control, and visibility into each step required to build, train, and deploy models. The data can be uploaded, new notebooks can be created, training and tuning of models, moving back and forth between steps to adjust experiments, comparing results, and further deploying the models to production all in one place and finally making users productive. All the Machine Learning development activities including the notebooks, experiment management, automatic model creation, debugging, and model and data drift detection can be usually performed within SageMaker Studio.
  • The Amazon SageMaker provides Elastic and Shareable Notebooks. The Amazon SageMaker provides managing compute instances to view, run, or share the notebook is tedious. The Amazon SageMaker Studio Notebooks are one-click Jupyter notebooks that can be spun up quickly. The underlying compute resources are fully elastic so users can easily dial up or down the available resources and the changes take place automatically in the background without interrupting the work. The notebooks can be easily shared with others in just a few clicks and users will get the same notebook and saved in the same place.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon SageMaker and the advantages of SageMaker.

Amazon SageMaker and its Advantages

    • It offers accessibility to Machine Learning

Amazon SageMaker enables more people to innovate with Machine Learning through the choice of tools—integrated development environments for the data scientists and no-code visual interfaces for many business analysts.

    • It offers preparation of data at scale

The Amazon SageMaker provides access, label, and helps in processing large amounts of structured data or tabular data and unstructured data (i.e. photos, video, and audio) for Machine Learning to users.

    • It accelerates Machine Learning development

The Amazon SageMaker helps in the acceleration of Machine Learning development by reducing the training time from hours to minutes with further optimized infrastructure. It also helps in boosting the team productivity up to 10 times with the purpose-built tools.

    • It streamlines the Machine Learning lifecycle

The Amazon SageMaker streamlines the machine learning lifecycle by automating and standardizing MLOps(Machine Learning Operations) practices across the organization which are using AWS services, to build, train, deploy, and manage models at a large scale.

    • It delivers High Performance

The Amazon SageMaker helps in the acceleration and streamlining of the machine learning lifecycle and thus it provides high performance in comparison to other available services in the industry.

    • It offers Low-cost Machine Learning

The Amazon SageMaker offers a low-cost Machine Learning solution as it is built on Amazon’s two decades of experience developing real-world machine learning applications including product recommendations, personalization, intelligent shopping, robotics, and voice-assisted devices. It has 54% less TCO and provides a 40% reduction in data labelling.

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