Course Overview Print

Building machine learning models is only the beginning. Operationalising them at scale requires robust pipelines, orchestration, and ongoing monitoring. This intermediate-level course equips participants with the practical skills to build, deploy, and maintain machine learning solutions using Amazon Web Services (AWS). Through hands-on labs and real-world scenarios, they will use tools such as Amazon SageMaker, Amazon EMR, and Model Monitor to develop secure and scalable ML systems. The course covers MLOps workflows, model tuning, data processing, and drift management in production environments.

Learning Outcomes

  • Understand machine learning principles and their application in AWS

  • Process and engineer data for ML tasks using AWS tools

  • Select and apply appropriate ML algorithms for given problem types

  • Implement scalable training and deployment pipelines with Amazon SageMaker

  • Monitor deployed models and respond to data drift or performance issues

  • Automate ML workflows using MLOps tools and practices

Key Topics

  • Amazon SageMaker and SageMaker Studio

  • Data preparation with Amazon S3, Glue, and EMR

  • Model training, tuning, and deployment

  • MLOps automation with SageMaker Pipelines

  • Model monitoring using SageMaker Model Monitor

  • Securing and scaling ML systems on AWS

 

Exam Details

This course is designed to build participants’ understanding of key concepts and domains covered in the AWS Machine Learning Engineer – Associate certification.

Exam FormatMultiple choice or multiple response
DeliveryPearson VUE testing centre
Duration130 minutes
Number of Questions65
Open BookNo
Passing Score720

To maximise success, participants are strongly encouraged to complement the course with additional self-study, revision of course materials, and dedicated practice before attempting the exam.

Course Outline

Day 1

Module 0: Course Introduction

Module 1: Introduction to Machine Learning (ML) on AWS Topic A: Introduction to ML

Topic B: Amazon SageMaker AI Topic C: Responsible ML

Module 2: Analysing Machine Learning (ML) Challenges Topic A: Evaluating ML business challenges

Topic B: ML training approaches Topic C: ML training algorithms

Module 3: Data Processing for Machine Learning (ML) Topic A: Data preparation and types

Topic B: Exploratory data analysis

Topic C: AWS storage options and choosing storage Module 4: Data Transformation and Feature Engineering

Topic A: Handling incorrect, duplicated, and missing data Topic B: Feature engineering concepts

Topic C: Feature selection techniques Topic D: AWS data transformation services

Lab 1: Analyse and Prepare Data with Amazon SageMaker Data Wrangler and Amazon EMR

Lab 2: Data Processing Using SageMaker Processing and the SageMaker Python SDK

Day 2

Module 5: Choosing a Modelling Approach

Topic A: Amazon SageMaker AI built-in algorithms Topic B: Amazon SageMaker Autopilot

Topic C: Selecting built-in training algorithms Topic D: Model selection considerations Topic E: ML cost considerations

Module 6: Training Machine Learning (ML) Models Topic A: Model training concepts

Topic B: Training models in Amazon SageMaker AI Lab 3: Training a model with Amazon SageMaker AI

Module 7: Evaluating and Tuning Machine Learning (ML) models Topic A: Evaluating model performance

Topic B: Techniques to reduce training time Topic C: Hyperparameter tuning techniques

Lab 4: Model Tuning and Hyperparameter Optimisation with Amazon SageMaker AI Module 8: Model Deployment Strategies

Topic A: Deployment considerations and target options Topic B: Deployment strategies

Topic C: Choosing a model inference strategy

Topic D: Container and instance types for inference Lab 5: Shifting Traffic

Day 3

Module 9: Securing AWS Machine Learning (ML) Resources

Topic A: Access control

Topic B: Network access controls for ML resources Topic C: Security considerations for CI/CD pipelines

Module 10: Machine Learning Operations (MLOps) and Automated Deployment

Topic A: Introduction to MLOps

Topic B: Automating testing in CI/CD pipelines Topic C: Continuous delivery services

Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry with Amazon SageMaker Studio

Module 11: Monitoring Model Performance and Data Quality Topic A: Detecting drift in ML models

Topic B: SageMaker Model Monitor

Topic C: Monitoring for data quality and model quality Topic D: Automated remediation and troubleshooting Lab 7: Monitoring a Model for Data Drift

Module 12: Course Wrap-up

*Important Note : Fees are subject to Singapore's prevailing Goods and Services Tax (GST).

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Machine Learning Engineering on AWS
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