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FRCAWS-21AWS

Machine Learning Engineering on AWS

Duration · 3 daysVirtual + In-PersonInstructor-Led

$2,025

Course Description

This intensive 3-day instructor-led course teaches engineers, developers, architects, and data professionals how to design, build, train, deploy, and operationalize machine learning solutions using services from Amazon Web Services (AWS). Students gain hands-on experience with scalable ML workflows, MLOps practices, model deployment, and production-ready AI architectures using AWS-native services.

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Audience Profile

This course is intended for:

  • Machine Learning Engineers
  • Data Engineers
  • Data Scientists
  • Software Developers
  • Cloud Engineers
  • DevOps Engineers
  • Solutions Architects
  • AI/ML Technical Leads

Prerequisites

Before enrolling, you should have:

  • Basic Python programming knowledge
  • Familiarity with cloud computing concepts
  • Understanding of data analytics fundamentals
  • Experience with AWS core services recommended
  • Introductory knowledge of machine learning concepts helpful

— What You'll Learn —

Learning Objectives

In this course, you will learn to:

  • 1Build end-to-end ML pipelines on AWS
  • 2Prepare and engineer datasets for ML workloads
  • 3Train and tune ML models using managed AWS services
  • 4Deploy models for batch and real-time inference
  • 5Implement MLOps workflows and CI/CD for ML
  • 6Monitor, secure, and optimize ML environments
  • 7Integrate generative AI and foundation models into applications
  • 8Design scalable ML architectures using AWS best practices

— Day-by-Day —

Course Outline

Day 1 — Foundations of Machine Learning Engineering on AWS

Module 1

Introduction to Machine Learning on AWS

Topics

  • Overview of ML engineering lifecycle
  • AWS AI/ML ecosystem overview
  • Managed vs custom ML workflows
  • Common enterprise ML use cases
  • Responsible AI concepts
  • AWS Services Covered
  • Amazon SageMaker
  • AWS Lambda
  • Amazon S3
  • AWS IAM

Lab

  • Explore AWS ML services
  • Configure ML development environment
  • Create SageMaker Studio workspace

Module 2

Data Engineering for Machine Learning

Topics

  • Data ingestion patterns
  • Data lakes and storage strategies
  • Data labeling and preprocessing
  • Feature engineering fundamentals
  • ETL pipelines for ML
  • AWS Services Covered
  • AWS Glue
  • Amazon Athena
  • Amazon Redshift
  • Amazon EMR

Lab

  • Build a machine learning dataset pipeline
  • Prepare and transform data using AWS Glue
  • Query datasets using Athena

Module 3

Machine Learning Model Development

Topics

  • ML model selection strategies
  • Supervised vs unsupervised learning
  • Training workflows in SageMaker
  • Built-in algorithms vs custom containers
  • Using Jupyter notebooks in SageMaker Studio
  • AWS Services Covered
  • Amazon SageMaker Studio
  • Amazon Elastic Container Registry (ECR)

Lab

  • Train a classification model
  • Use SageMaker notebooks
  • Evaluate model performance metrics

Module 4

Feature Engineering and Feature Stores

Topics

  • Feature extraction techniques
  • Data normalization and encoding
  • Managing reusable features
  • Feature governance and consistency
  • AWS Services Covered
  • Amazon SageMaker Feature Store

Lab

  • Create and manage ML features
  • Build reusable feature pipelines

Day 2 — Advanced ML Workflows and MLOps

Module 5

Hyperparameter Optimization and Automated ML

Topics

  • Hyperparameter tuning strategies
  • Automated model selection
  • Experiment tracking
  • Model explainability
  • AWS Services Covered
  • Amazon SageMaker Automatic Model Tuning
  • Amazon SageMaker Autopilot

Lab

  • Run hyperparameter tuning jobs
  • Compare multiple model experiments

Module 6

MLOps and CI/CD for Machine Learning

Topics

  • MLOps fundamentals
  • Model versioning
  • CI/CD pipelines for ML
  • Infrastructure as Code for ML environments
  • Automated retraining workflows
  • AWS Services Covered
  • AWS CodePipeline
  • AWS CodeBuild
  • AWS CloudFormation
  • Amazon SageMaker Pipelines

Lab

  • Build an automated ML pipeline
  • Configure model retraining automation
  • Deploy ML infrastructure as code

Module 7

Model Deployment and Inference

Topics

  • Real-time inference
  • Batch inference
  • Edge deployment concepts
  • Multi-model endpoints
  • Serverless inference
  • AWS Services Covered
  • Amazon SageMaker Endpoints
  • AWS Fargate
  • Amazon API Gateway

Lab

  • Deploy a model endpoint
  • Build an inference API
  • Test batch prediction workflows

Module 8

Monitoring, Security, and Governance

Topics

  • ML observability
  • Drift detection
  • Security best practices
  • Encryption and compliance
  • Cost optimization
  • AWS Services Covered
  • Amazon CloudWatch
  • AWS KMS
  • AWS CloudTrail
  • Amazon SageMaker Model Monitor

Lab

  • Configure model monitoring
  • Detect model drift
  • Secure ML endpoints

Day 3 — Generative AI, Advanced Architectures, and Capstone

Module 9

Generative AI and Foundation Models on AWS

Topics

  • Introduction to generative AI
  • Foundation models overview
  • Prompt engineering concepts
  • Retrieval-Augmented Generation (RAG)
  • AI application architectures
  • AWS Services Covered
  • Amazon Bedrock
  • Amazon Titan Models
  • Amazon OpenSearch Service

Lab

  • Build a generative AI chatbot
  • Implement prompt workflows
  • Connect vector search for RAG

Module 10

Scalable ML Architectures

Topics

  • Distributed training strategies
  • GPU and accelerated computing
  • High-availability ML systems
  • Event-driven ML architectures
  • Multi-account ML environments
  • AWS Services Covered
  • Amazon EC2 P-Series Instances
  • AWS Step Functions
  • Amazon EventBridge

Lab

  • Build scalable ML workflow orchestration
  • Configure distributed training environment

Module 11

Production ML Engineering Best Practices

Topics

  • ML system reliability
  • Operational excellence
  • Cost management
  • Disaster recovery planning
  • Enterprise governance models
  • Group Discussion
  • Real-world enterprise ML case studies
  • ML engineering anti-patterns
  • Architecture review workshop

Module 12

Capstone Project

  • Student Project
  • Students design and implement a complete ML engineering solution on AWS.
  • Capstone Activities
  • Build an end-to-end ML pipeline
  • Train and optimize a model
  • Deploy inference endpoints
  • Configure monitoring and governance
  • Present architecture and results
  • Included Hands-On Labs
  • Students complete guided labs covering:
  • SageMaker Studio setup
  • Data preparation pipelines
  • ML model training
  • Hyperparameter tuning
  • Model deployment
  • CI/CD for ML
  • MLOps automation
  • Generative AI applications
  • Model monitoring and drift detection

— Next Steps —

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— Additional Details —

What else is included

Suggested Course Materials

  • Student guide
  • Instructor slides
  • Hands-on lab manual
  • AWS architecture diagrams
  • Sample datasets
  • Capstone project workbook

Note: Course outlines are provided as a general guide. Content, pacing, labs, and instructional emphasis may vary based on instructor expertise, student experience levels, and customer-specific learning objectives.

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