Official AWS Course

MLOps Engineering on AWS Training

Operationalize machine learning on AWS with automated pipelines, deployment strategies, A/B testing, and drift monitoring. 3-day official course with hands-on labs and an MLOps action plan.

👥 1390 Enrolled ⏱️ 3 Days 💼 Intermediate Level ⭐ 4.9 ( 289 ) Reviews

500+Courses

10+ YrsExperience

95K+Global Learners

Live Online (Public Batch)

Instructor-led group class, fixed schedule

USD 1799

Private Group

Train your whole team, on your dates

Custom Quote

For Teams

Proceed to Buy Request Info

📄 Download Course PDF

100% Satisfaction Guarantee · Guaranteed to Run · Secure Payment

Course Overview

The hardest problems in production machine learning are rarely mathematical. They are handoffs: data engineers passing to data scientists, data scientists passing to developers, developers passing to operations, and something breaking at every boundary. MLOps exists to fix that, and this course teaches it as a discipline rather than a toolset.

The curriculum extends DevOps practice into the ML lifecycle and is built around a four-level MLOps maturity framework, concentrating on the first three levels: initial, repeatable, and reliable. Throughout, it treats data, model, and code as three assets that must be versioned and kept in step, because a model is only reproducible if all three are.

Across three days you will compare DevOps with MLOps and understand where the analogy breaks, evaluate security and governance requirements for an ML use case, set up experimentation environments with Amazon SageMaker, and learn versioning practices that keep ML assets intact. You will work through options for building a full CI/CD pipeline in an ML context, apply best practices for automated packaging, testing, and deployment, and cover deployment operations including model packaging, inference, production variants, deployment strategies, edge deployment, and deployment security.

The final stretch covers monitoring: why it matters, designing for it from the start, detecting drift in input data, monitoring for bias and for resource consumption and latency, and integrating human-in-the-loop review of model results in production. You will build an automated ML solution that tests, packages, and deploys a model, detects performance degradation, and retrains on newly acquired data.

A running thread through the course is the MLOps Action Plan Workbook, a group activity revisited after each module so you leave with a plan for your own organization rather than just notes.

Mode of Training

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

Upcoming Schedules

Start Date	Time	Duration	Mode	Price
Sep 02, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR
Sep 16, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR
Sep 30, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR
Oct 14, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR
Oct 28, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR

+ View more schedules

What you will learn

Explain the benefits of MLOps and where DevOps and MLOps differ
Describe the ML workflow from an MLOps perspective and the role communication plays in it
Evaluate security and governance requirements for an ML use case and describe mitigation strategies
Set up experimentation environments for MLOps with Amazon SageMaker
Apply best practices for versioning and maintaining the integrity of ML assets across data, model, and code
Describe the options for building a full CI/CD pipeline in an ML context
Build an automated process that builds, trains, tests, and deploys models
Build an automated process that retrains a model when the model code changes
Apply deployment operations including packaging, inference, production variants, and edge deployment
Identify security threats in ML and explain mitigation approaches
Detect drift in input data and monitor models for bias, resource consumption, and latency
Integrate human-in-the-loop review of model results in production

Who Should Attend This Course?

Professionals responsible for productionizing machine learning models on AWS, including:

DevOps engineers taking on ML workloads
ML platform and data platform engineers
Data scientists who need their models to survive contact with production
Software developers and operations staff supporting ML deployment

Prerequisites

Required:

Recommended:

The Elements of Data Science, or equivalent experience
Machine Learning Terminology and Process

The prerequisite list is heavier than most AWS courses because this one sits at the intersection of three disciplines. You need working AWS knowledge, familiarity with SageMaker and ML workflows, and real exposure to DevOps practice and CI/CD. Attendees strong in only one of the three tend to struggle.

📞 Talk to a Learning Advisor

Certification

This course supports preparation for Exam MLA-C01, leading to the AWS Certified Machine Learning Engineer – Associate credential, which validates the ability to build, operationalize, deploy, and maintain machine learning solutions and pipelines on AWS. The exam is booked and paid for separately.

A note for anyone researching this area: the AWS Certified Machine Learning – Specialty exam (MLS-C01) that older listings referenced was retired on 31 March 2026. MLA-C01 is the current associate-level path.

📘 MLOps Engineering on AWS Outline

Machine learning operations and their goals
The path from DevOps to MLOps
Scope, and an MLOps view of the machine learning workflow
Communication across roles
The value of MLOps, with case examples

Building, training, and evaluating models
Automation approaches
Apache Airflow
Kubernetes integration for MLOps
Amazon SageMaker for MLOps, with demonstration
Lab: bring your own algorithm to an MLOps pipeline
Lab: code and serve your ML model with AWS CodeBuild
Group activity: MLOps Action Plan Workbook

Introduction to deployment operations
Model packaging and inference
Lab: deploy your model to production
SageMaker production variants
Deployment strategies
Deploying to the edge
Deployment security
Lab: conduct A/B testing
Group activity: MLOps Action Plan Workbook

Why monitoring matters, and monitoring by design
Lab: monitor your ML model
Human-in-the-loop review
Amazon SageMaker Model Monitor, with demonstration
Solving the problems monitoring surfaces
Group activity: MLOps Action Plan Workbook

Course review
Final MLOps Action Plan Workbook session

❓ Frequently Asked Questions

They overlap, and the difference is one of emphasis. Machine Learning Engineering on AWS covers the fuller ML lifecycle including algorithm selection, model training, and tuning, alongside deployment and monitoring. MLOps Engineering on AWS assumes the model exists and concentrates on operationalizing it: maturity levels, asset versioning, CI/CD, deployment operations, and monitoring. If your team already builds models and cannot ship them reliably, this is the more targeted course. If you need the whole lifecycle, the other one is broader.

A way of assessing how mature an organization's ML operations are, from ad hoc work through to fully automated. The course focuses on the first three levels: initial, repeatable, and reliable. It is a useful diagnostic, because it gives teams a shared language for where they are now and what the next realistic step is.

Because MLOps genuinely sits between three disciplines. AWS asks for AWS Technical Essentials, Practical Data Science with Amazon SageMaker, and DevOps Engineering on AWS as required preparation. The course does not stop to teach SageMaker basics or CI/CD fundamentals, so gaps in either area become obvious quickly.

A group activity that runs after each module, where you apply the lesson to your own organization and build up a practical action plan through the week. It is one of the more useful features of the course, particularly for teams attending together, because you leave with something you can act on rather than a set of notes.

Yes. You bring your own algorithm into an MLOps pipeline, serve a model with AWS CodeBuild, deploy to production, run A/B testing, and set up monitoring. The emphasis throughout is on automation that survives being handed to someone else.

Yes, and this course benefits from it more than most. The action plan activity works far better when everyone in the room shares the same architecture, constraints, and handoff problems.