Amazon SageMaker is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It 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 machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, SageMaker offers flexible distributed training options that adjust to your specific workflows. Deploy a model into a secure and scalable environment by launching it with a few clicks from SageMaker Studio or the SageMaker console.


Introducing Amazon SageMaker



ML Model Life Cycle










Features of using Amazon SageMaker?

✔ To make it easier to get started, Amazon SageMaker JumpStart provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks.

 Prepare, build, train, and deploy high-quality machine learning models quickly by bringing together a broad set of capabilities purpose-built for machine learning.

✔ Amazon SageMaker is available for free, for 2 months, as part of the AWS Free Tier program. Users can get access to 250 hours per month of ml.t3.medium notebooks usage with the Free Tier.



Getting Started with Amazon SageMaker

Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning. The solutions are fully customizable and supports one-click deployment and fine-tuning of more than 150 popular open source models such as natural language processing,   object detection, and image classification models. Popular solutions include:

  • Extract & Analyze Data
    Automatically extract, process, and analyze documents for more accurate investigation and faster decision making.

  • Fraud Detection
    Automate detection of suspicious transactions faster and alert your customers to reduce potential financial loss.

  • Churn Prediction
    Predict likelihood of customer churn and improve retention by honing in on likely abandoners and taking remedial actions such as promotional offers.

  • Personalized Recommendations
    Deliver customized, unique experiences to customers to improve customer satisfaction and grow your business rapidly.



Helpful Links
  • Available Studio Instance Types (Amazon SageMaker Studio)
    Amazon SageMaker Studio notebooks run on Amazon Elastic Compute Cloud (Amazon EC2) instances. The following Amazon EC2 instance types are available for use with Studio notebooks.

  • Create a SageMaker Jupyter Notebook
    To start scripting for training and deploying your model, create a Jupyter notebook in the SageMaker notebook instance. Using the Jupyter notebook, you can conduct machine learning (ML) experiments for training and inference while accessing the SageMaker features and the AWS infrastructure.