Official AWS Course

Machine Learning Engineering on AWS Training

Learn to build, deploy, and operationalize machine learning on AWS with SageMaker AI, MLOps pipelines, and drift monitoring.

👥 1837 Enrolled ⏱️ 3 Days 💼 Intermediate Level ⭐ 5 ( 284 ) Reviews

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10+ YrsExperience

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Instructor-led group class, fixed schedule

USD 1799

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Course Overview

Plenty of organizations can train a model. Far fewer can keep one running reliably in production, retrain it when the data shifts, and prove it is still behaving. That gap between model development and production deployment is what machine learning engineering exists to close, and it is what this course teaches.

Over three days you will build, deploy, orchestrate, and operationalize ML solutions at scale on AWS, working with Amazon SageMaker AI and analytics services such as Amazon EMR. The course moves through the full engineering lifecycle rather than the research one. You will start with ML fundamentals on AWS and responsible ML, then analyze business challenges to choose training approaches and algorithms sensibly.

The data work follows: preparation and exploratory analysis, choosing storage on AWS, handling incorrect, duplicated, and missing data, feature engineering and feature selection, and the AWS services that do the transformation. From there you select a modeling approach, weighing built-in SageMaker algorithms, Autopilot, model interpretability, and cost, then train models and tune them with hyperparameter optimization and techniques for reducing training time.

The last stretch is where engineering separates from data science. You will design deployment strategies and choose inference options, containers, and instance types, secure ML resources with access control and network controls, build CI/CD pipelines with automated testing, and implement monitoring that detects drift in data and model quality with automated remediation. Seven hands-on labs run throughout, including A/B traffic shifting, SageMaker Pipelines with Model Registry, and monitoring a model for data drift.

Mode of Training

🏫 Classroom 💻 Live Online 🧪 Blended 👨‍👩‍👧‍👦 Private Group

Upcoming Schedules

Start Date	Time	Duration	Mode	Price
Aug 26, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR
Sep 09, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR
Sep 23, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR
Oct 07, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR
Oct 21, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR
Nov 04, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR

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What you will learn

Explain ML fundamentals and their application in the AWS Cloud
Process, transform, and engineer data for ML tasks using AWS services
Select appropriate ML algorithms and modeling approaches based on problem requirements and interpretability needs
Design and implement scalable ML pipelines for training, deployment, and orchestration
Create automated CI/CD pipelines for ML workflows
Apply appropriate security measures to ML resources on AWS
Implement monitoring strategies for deployed models, including data drift detection

Who Should Attend This Course?

Professionals who build, deploy, and operationalize machine learning models on AWS, including:

Machine learning engineers, both practising and in training, who may have limited AWS experience
DevOps engineers building the automation behind ML delivery
Cloud developers and SysOps engineers moving into ML infrastructure
Data scientists who want to own their models through to production rather than handing them off

Prerequisites

Familiarity with basic machine learning concepts
Working knowledge of Python and common data science libraries such as NumPy, Pandas, and Scikit-learn
Basic understanding of cloud computing concepts and familiarity with AWS
Experience with version control such as Git is useful but not required

This is an intermediate course. You do not need deep AWS expertise to attend, but you do need to be comfortable writing Python and to understand what a model is and why it needs training.

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Certification

This course aligns to Exam MLA-C01, leading to the AWS Certified Machine Learning Engineer – Associate credential. The exam validates the ability to build, operationalize, deploy, and maintain machine learning solutions and pipelines on AWS, which maps closely to what this course covers: data preparation, model training and tuning, deployment infrastructure, CI/CD orchestration, monitoring, and securing ML systems. The exam is booked and paid for separately from the course.

A note for anyone comparing course listings: some pages still describe this course as preparation for AWS Certified Machine Learning – Specialty (MLS-C01). That exam was retired on 31 March 2026 and can no longer be taken. Existing holders keep their certification for three years from the date it was earned, but MLA-C01 is the current path.

📘 Machine Learning Engineering on AWS Outline

Introduction to ML
Amazon SageMaker AI
Responsible ML

Evaluating ML business challenges
ML training approaches
ML training algorithms

Data preparation and data types
Exploratory data analysis
AWS storage options and choosing the right one

Handling incorrect, duplicated, and missing data
Feature engineering concepts and feature selection techniques
AWS data transformation services
Lab: analyze and prepare data with SageMaker Data Wrangler and Amazon EMR
Lab: data processing using SageMaker Processing and the SageMaker Python SDK

SageMaker AI built-in algorithms and how to select among them
Amazon SageMaker Autopilot
Model selection considerations
ML cost considerations

Model training concepts
Training models in Amazon SageMaker AI
Lab: training a model with Amazon SageMaker AI

Evaluating model performance
Techniques to reduce training time
Hyperparameter tuning techniques
Lab: model tuning and hyperparameter optimization with Amazon SageMaker AI

Deployment considerations and target options
Deployment strategies and choosing an inference strategy
Container and instance types for inference
Lab: shifting traffic with A/B deployment

Access control
Network access controls for ML resources
Security considerations for CI/CD pipelines

Introduction to MLOps
Automating testing in CI/CD pipelines
Continuous delivery services
Lab: using SageMaker Pipelines and Model Registry with SageMaker Studio

Detecting drift in ML models
SageMaker Model Monitor
Monitoring for data quality and model quality
Automated remediation and troubleshooting
Lab: monitoring a model for data drift

❓ Frequently Asked Questions

Different audience and different emphasis. The SageMaker Studio course is advanced and aimed at experienced data scientists working inside the Studio environment across the ML lifecycle. This course is intermediate and aimed at the engineering side: pipelines, CI/CD, deployment strategy, security, and monitoring. If your job is building models, that one fits. If your job is making models run reliably in production, this one does.

MLA-C01, the AWS Certified Machine Learning Engineer – Associate exam. This is the current associate-level ML certification, and it replaced the retired Machine Learning – Specialty exam. Plan on additional self-study and hands-on practice alongside the course before booking the exam.

No, and that is a genuine difference from the Studio course. This one assumes familiarity with basic ML concepts and working Python, not years of modeling experience. It is well suited to DevOps and cloud engineers moving into ML, and to ML engineers who are newer to AWS specifically.

Seven labs across the three days, covering data preparation with Data Wrangler and EMR, SageMaker Processing, model training, hyperparameter tuning, A/B traffic shifting, Pipelines with Model Registry, and drift monitoring. Alongside those are demonstrations and group exercises.

Automating the parts of the ML lifecycle that break when done manually: testing inside CI/CD pipelines, continuous delivery services, orchestrating training and deployment through SageMaker Pipelines, and managing model versions and approval through Model Registry. It is paired with the monitoring module, since automated deployment without drift detection just lets problems reach production faster.

Yes, and it works well for mixed teams. The course deliberately spans data science and DevOps concerns, so bringing both groups together tends to surface the handoff problems an organization actually has.