vicrojo/awesome-sagemaker

A curated list of references for Amazon SageMaker

AWSome SageMaker

A curated list of awesome references for Amazon SageMaker.

Contents

• Getting Started
• Core Functionalities
• ML Domains
• ML Operations
• Advanced Topics
• Enterprise & Architecture
• Training & Certification
• Community
• Books
• What's New
• Contributions

Getting Started

• Introduction to Amazon SageMaker
• SageMaker Example Notebooks
• Amazon SageMaker 101 Workshop
• SageMaker Immersion Day (hands-on labs)

Core Functionalities

• Developer Experience

  • Onboard Quickly to Amazon SageMaker Studio
  • Hosting VS Code on SageMaker
  • Using RStudio on SageMaker

• SDKs & Infrastructure-as-Code

  • SageMaker Python SDK
  • Boto3 and AWS CLI
  • CloudFormation
  • CDK
  • Terraform

• Containers & Frameworks

  • Supported pre-built containers/frameworks (script mode)
  • Deep Learning Containers
  • Spark Containers
  • Using Script Mode with Amazon SageMaker
  • Using custom training containers
  • Training toolkit and Inference toolkit
  • How Amazon SageMaker Provides Training Information
  • Create reusable containers for R

• Jobs

  • SageMaker Processing
  • Training - end-to-end example with Scikit-Learn
  • Training - end-to-end example with Script Mode
  • Using local mode

• Inference

  • Amazon SageMaker Inference explained: Which style is right for you?
  • Introduction to Amazon SageMaker Serverless Inference
  • Running Triton Inference Server with Amazon SageMaker

ML Domains

• Computer Vision

  • Using the TensorFlow 2 Object Detection API with Amazon SageMaker
  • Object detection with Detectron2 on Amazon SageMaker

• Natural Language Processing

  • Hugging Face on Amazon SageMaker
  • Hugging Face on Amazon SageMaker Tutorial - Part 1, Part 2, Part 3

• Data Labeling

  • Introducing Amazon SageMaker Ground Truth Plus
  • Labeling data with Label Studio on SageMaker

• AutoML

  • Using AutoML to create high-quality models with just a few clicks
  • Using AutoML for Common Financial Services Use Cases

• Model Monitoring

• Explainability & Bias Detection

  • Build trustworthy ML detection and monitor bias in ML models

• Low-Code/No-Code

  • Introduction to Amazon SageMaker Canvas
  • SageMaker Canvas Immersion Day
  • AWS Machine Learning Low-Code Immersion Day

• R

  • RStudio on SageMaker
  • Overview of possible ways of running R workloads on SageMaker

ML Operations

• MLOps Foundations

  • MLOps foundation roadmap for enterprises with Amazon SageMaker
  • Implementing MLOps practices with Amazon SageMaker, featuring Vanguard
  • MLOps Multi Account Setup with AWS CDK

• SageMaker Pipelines

  • Automate MLOps with SageMaker Projects
  • Introducing Amazon SageMaker Pipelines
  • Building, automating, managing, and scaling ML workflows using Amazon SageMaker Pipelines
  • Multi-branch training MLOps pipeline using AWS

• Using Third-Party

  • Create Amazon SageMaker projects using third-party source control and Jenkins
  • Build MLOps workflows with Amazon SageMaker projects, GitLab, and GitLab pipelines
  • MLOps with MLFlow and Amazon SageMaker Pipelines
  • Scaling MLOps with resilient pipelines

• Experiment Tracking & Model Registry

  • Managing your machine learning lifecycle with MLflow and Amazon SageMaker
  • Improve ML developer productivity with Weights & Biases and Amazon SageMaker

• Data Versioning & Feature Store

  • Amazon SageMaker Feature Store Workshop
  • Track your ML experiments with DVC and Amazon SageMaker Experiments
  • Getting started with Amazon SageMaker Feature Store
  • Understanding the key capabilities of Amazon SageMaker Feature Store
  • Scale ML feature ingestion using Amazon SageMaker Feature Store
  • Extend model lineage to include ML features using Amazon SageMaker Feature Store
  • Control access to Amazon SageMaker Feature Store offline using AWS Lake Formation

Advanced Topics

• Large Scale Data Processing

  • Using Apache Spark on Amazon EMR with SageMaker
  • Scalable data preparation using Amazon SageMaker Studio notebooks - AWS Virtual Workshop
  • SageMaker Studio integration with EMR Workshop
  • Spark in Processing Jobs
  • Using Dask with SageMaker Processing
  • Build accurate ML training datasets using point-in-time queries with Amazon SageMaker Feature Store and Apache Spark

• Distributed Training

  • Choose the best data source for your Amazon SageMaker training job
  • Scalable Medical Computer Vision Model Training - Part 1 and Part 2

• Hardware Acceleration

  • Choosing the right GPU for deep learning on AWS

• Edge Deployments

  • Industrial defect detection with computer vision using Amazon SageMaker
  • Using Amazon SageMaker Edge Manager and AWS IoT Greengrass V2
  • ML@Edge with SageMaker Neo & Edge Manager - Getting Started
  • ML@Edge with SageMaker Edge Manager - Workshop
  • MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass

• Debugging

  • Use TensorBoard in Amazon SageMaker Studio
  • Visualize Amazon SageMaker Training Jobs with TensorBoard
  • Deep Dive on Amazon SageMaker Debugger & Amazon SageMaker Model Monitor

• Model Monitor

  • The 4-types of model monitoring with SageMaker
  • Retrain your model when data drift is detected

Enterprise & Architecture

• Architecture Best Practices

  • Architecture Best Practices for Machine Learning
  • AWS Well-Architected Machine Learning Lens

• ML Platform Setup

  • Setting up secure, well-governed machine learning environments on AWS
  • Enabling self-service provisioning of Amazon SageMaker Studio resources
  • Industrializing an ML platform with Amazon SageMaker Studio
  • Scaling Enterprise ML Platforms with Modern Cloud Operations
  • Access SageMaker Studio via an external identity provider

• Security

  • Security in Amazon SageMaker
  • Dive deep into Amazon SageMaker Studio Notebooks architecture

• Cost Tracking & Control

  • SageMaker Pricing
  • Decrease Your Machine Learning Costs with Instance Price Reductions and Savings Plans for Amazon SageMaker
  • Machine Learning Savings Plans
  • Automatically shutdown idle resources on SageMaker Studio

Training & Certification

• MOOCs

  • Getting Started with AWS Machine Learning
  • Practical Data Science on the AWS Cloud Specialization
  • Machine Learning University

• Digital & Classroom

  • AWS Certified Machine Learning - Specialty
  • Machine Learning Learning Plan
  • AWS Classroom Training

• Tutorials

  • AWS hands-on tutorials

Community

• DataScienceOnAWS
• Data Science on AWS GitHub
• Amazon SageMaker Fridays
• AWS Machine Learning Blog
• AWS ML School
• AWS events
• AWS re:Post
• Stack Overflow

Books

• Learn Amazon SageMaker - Second Edition
• Data Science on AWS

News

• What's New with Machine Learning on AWS?

Contributions

Maintainers

• Bruno Pistone
• Othmane Hamzaoui
• Sofian Hamiti
• Georgios Schinas