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

• For Free Tier Limits: https://aws.amazon.com/free
• For SageMaker Pricing: https://aws.amazon.com/sagemaker/pricing/

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.

2. Console: SageMaker Notebook Instances

• Uses familiar Jupyter and JuypterLab interfaces

For Advanced Users

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

4. 3rd party integrations: Kubeflow & Kubernetes operators (can setup environment to use SageMaker operators for training and inference).