MLOps Engineering on AWS course material links

Please not that this is not a prep course for the “AWS Certified Machine Learning Engineer - Associate”, “AWS Certified Machine Learning - Specialty” or any other certification!

This document contains links mentioned in the “MLOps Engineering on AWS” course.

Module 0: Course Overview

Full name: MLOps Engineering on AWS

Product code: 200-MLPOPS-20

These links are based on the course version 2.0.4.

The official course material: eVantage Bookshelf (VitalSource)

Additional material: AWS Skill Builder

Module 1: Introduction to MLOps

Websites

• MLOps Principles
• Hidden Technical Debt in Machine Learning Systems
• Amazon SageMaker Studio Classic
• Secure Data Science With Amazon SageMaker Studio Reference Architecture
• Amazon EMR
• Amazon Athena
• AWS Glue
• AWS CodeCommit

Module 2: Initial MLOps /Experimentation Environments in SageMaker Studio

Websites

• Use Amazon SageMaker Studio Classic Notebooks
• How Are Amazon SageMaker Studio Classic Notebooks Different from Notebook Instances?
• Use Docker containers to train and deploy models
• Use Amazon SageMaker built-in algorithms or pretrained models
• Use Pre-built SageMaker Docker images
• Machine Learning Frameworks and Languages
• Available Deep Learning Containers Images
• What are AWS Deep Learning Containers?
• Create a container with your own algorithms and models
• Adapting your own Docker container to work with SageMaker
• Build container images from your Studio notebooks
• SageMaker Environment Variables and Default Paths for Training Storage Locations
• Adapting your own Docker container to work with SageMaker
• Bring your own pre-trained MXNet or TensorFlow models into Amazon SageMaker
• Use Docker containers to train and deploy models
• Dive deep into Amazon SageMaker Studio Classis Notebooks architecture
• Install External Libraries and Kernels in Amazon SageMaker Studio Classic
• Quick setup to Amazon SageMaker
• Automate a centralized deployment of Amazon SageMaker Studio with AWS Service Catalog
• SageMaker Studio Permissions Required to Use Projects
• Create Amazon SageMaker projects with image building CI/CD pipelines
• Train, deploy, and evaluate pretrained models with SageMaker JumpStart
• Collaborate with shared spaces
• Persona reference
• ML activity reference
• Configuring Amazon SageMaker Studio for teams and groups with complete resource isolation
• Best Practicies for Developing Life-Cycle Configuration Scripts for SageMaker Studio
• Install External Libraries and Kernels in Amazon SageMaker Studio Classic
• SageMaker Studio Classic Lifecycle Configuration Samples
• Verify lifecycle configuration process from CloudWatch Logs
• SageMaker Studio Classic Lifecycle Configuration Samples
• Customize Amazon SageMaker Studio using Lifecycle Configurations
• Using CodeWhisperer and CodeGuru extensions with SageMaker
• Amazon SageMaker Studio and SageMaker Notebook Instance now come with JupyterLab 3 notebooks to boost developer productivity
• MLOps foundation roadmap for enterprises with Amazon SageMaker
• AWS Solutions Library

Module 3: Repeatable MLOps / Repositories

Websites

• Using versioning in S3 buckets
• Lake Formation tag-based access control
• Amazon SageMaker Feature Store
• Create, store, and share features with Feature Store
• Amazon SageMaker Data Wrangler
• Batch ingestion with Amazon SageMaker Feature Store Spark
• Feature Store Feature Processor SDK
• Unlock ML insights using the Amazon SageMaker Feature Store Feature Processor
• Create and Manage SageMaker Pipelines
• Register and Deploy Models with Model Registry
• Register a Model Version
• Add a Git Repository to Your Amazon SageMaker Account
• Authentication and access control for AWS CodeCommit

Module 4: Repeatable MLOps / Orchestration

Websites

• MLOps Workload Orchestrator on AWS
• Use the XGBoost algorithm with Amazon SageMaker
• Orchestrate Jobs to Train and Evaluate Models with Amazon SageMaker Pipelines
• IAM: Pass an IAM role to a specific AWS service
• IAM roles
• Using the SageMaker Python SDK
• Pipeline Parameters
• Pass Data Between Steps
• Caching Pipeline Steps
• Selective execution of pipeline steps
• Retry Policy for Pipeline Steps
• Create and Manage SageMaker Pipelines
• Amazon SageMaker pricing
• Deploy models with Amazon SageMaker Serverless Inference
• Asynchronous inference
• Use batch transform to run inference with Amazon SageMaker
• Amazon SageMaker pricing
• Best practices for deploying models on SageMaker Hosting Services
• Amazon SageMaker Model Building Pipelines steps
• Kubernetes Orchestration
• Manage SageMaker with Step Functions
• Train a Machine Learning Model
• Tune a Machine Learning Model
• Preprocess data and train a machine learning model
• What Is AWS Service Catalog?
• Use SageMaker-Provided Project Templates
• SageMaker Studio Permissions Required to Use Projects
• AWS Managed Policies for SageMaker projects and JumpStart
• Orchestrate XGBoost ML Pipelines with Amazon Managed Workflows for Apache Airflow
• Managing your machine learning lifecycle with MLflow and Amazon SageMaker
• Securing MLflow in AWS: Fine-grained access control with AWS native services
• Define customized permissions in minutes with Amazon SageMaker Role Manager
• New ML Governance Tools for Amazon SageMaker – Simplify Access Control and Enhance Transparency Over Your ML Projects
• Improve governance of your machine learning models with Amazon SageMaker
• Amazon SageMaker controls
• Securing Amazon SageMaker Studio connectivity using a private VPC
• Connect to Amazon SageMaker resources from within a VPC

Module 5: Reliable MLOps / Scaling and testing

Websites

• Host models along with pre-processing logic as serial inference pipeline behind one endpoint
• Use a multi-container endpoint with direct invocation
• Configuring autoscaling inference endpoints in Amazon SageMaker
• Step scaling policies for Application Auto Scaling
• Organizing Your AWS Environment Using Multiple Accounts
• Bootstrapping multiple AWS accounts for AWS CDK using CloudFormation StackSets
• Create Custom Project Templates
• Benefits of using multiple AWS accounts
• Operating model
• AWS Well-Architected
• Machine Learning Lens
• Testing phase
• Shadow tests
• Amazon Sagemaker API Reference: InvokeEndpoint
• Amazon Sagemaker API Reference: CreateEndpoint
• Production variants
• CloudWatch Metrics
• Update the Approval Status of a Model
• Perform Shadow Tests to Compare Inference Performance Between ML Model Variants
• Monitor Amazon SageMaker with Amazon CloudWatch
• Getting started with CodeWhisperer

Videos

• Who Did This? Track Code Recommendations With Amazon CodeWhisperer

Module 6: Reliable MLOps / Monitoring

Websites

• Monitor Amazon SageMaker with Amazon CloudWatch
• Visual explanations of core machine learning concepts
• Transform responsible AI from theory into practice
• Amazon CloudWatch
• Amazon SageMaker Studio
• Model quality metrics and Amazon CloudWatch monitoring
• SageMaker Model Monitor - visualizing monitoring results
• Schedule monitoring jobs
• Monitor Bias Drift for Models in Production
• Extend model lineage to include ML features using Amazon SageMaker Feature Store
• Amazon SageMaker pricing
• Monitor Bias Drift for Models in Production
• Measure Pre-training Bias
• Measure Post-training Data and Model Bias
• Schedule model quality monitoring jobs
• Querying Lineage Entities

Module 7: Course Wrap-up

Websites

• MLOps foundation roadmap for enterprises with Amazon SageMaker
• Create Custom Project Templates
• Custom Project Templates in SageMaker
• Amazon SageMaker for MLOps
• MLOps Workload Orchestrator on AWS
• AWS Certification exam preparation
• AWS Certification
• AWS Recertification
• All AWS Certification exams
• Schedule an Exam
• AWS Skill Builder subscription
• AWS Training and Certification
• AWS Online Tech Talks
• AWS Ramp-Up Guides

Courses

• AWS Skill Builder
• AWS Builder Labs
• AWS Workshops