Ref: https://learn.cantrill.io/courses/1820301/lectures/42194151

• Summary:
• Contents:

Amazon SageMaker 101

• 🔧 Fully-managed Machine Learning (ML) service
  • 💡 Actually a collection of many features and products, bundled together by AWS
  • Covers whole ML model lifecycle:
    • Fetch, clean, and prepare data
    • Train, evaluate, deploy, and monitor ML models
• Key features
  • SageMaker Studio = IDE for ML model lifecycle
    • Build, train, debug and monitor ML models
  • SageMaker Domain = isolation/grouping for a particular project
    • Contains EFS volume, users, apps, policies, VPCs…
  • Containers = ML environments
    • Docker containers deployed to EC2 instances optimized for ML
    • Specific OS, libraries, and tooling for ML workloads
  • Hosting → Deploy endpoints for ML models
  • ‼️ SageMaker itself has no cost!
    • …billed for created resources → complex pricing!!
    • 💡 ML resources are usually complex and high performant, it can get pricey! For extra reference, check: https://aws.amazon.com/sagemaker/pricing/