Published on

Introduction to Amazon SageMaker

Table of Contents

What is Amazon SageMaker?

  • Fully managed (store and share your data without having to build and manage your own servers) machine learning (ML) service.
  • Handle full MDLC workflow (build, train, and deploy ML models into a production-ready hosted environment).
  • Provides managed ML algorithms with built-in support for bring-your-own-algorithms and frameworks.

Pricing of Amazon SageMaker

How to Use Amazon SageMaker

You have several options for how you can use Amazon SageMaker.

For Beginner Users

  1. IDE: SageMaker Studio
  • Web-based UI
  • Seamless integration with deep learning and data science environments and scalable compute resources for training, inference, and other ML operations.
  • No need for complex systems admin and security processes.
  • Fully control data access and resource provisioning for users.
  1. Console: SageMaker Notebook Instances
  • Uses familiar Jupyter and JuypterLab interfaces

For Advanced Users

  1. Command line & SDK: AWS CLI, boto3, & SageMaker Python SDK

  2. 3rd party integrations: Kubeflow & Kubernetes operators (can setup environment to use SageMaker operators for training and inference).