MLOps Engineering on AWS Training Course

Name: MLOps Engineering on AWS
Price: 2025.00 USD
Availability: InStock
Rating: 4.5 (1200 reviews)

Price
$2,025.00 USD

Duration
3 Days

Delivery Methods
Virtual Instructor Led
Private Group

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Course Overview

What if your machine learning models could move from development to deployment with the same confidence and consistency as your software releases?

MLOps Engineering on AWS is a hands-on, instructor-led course that helps you bring structure, automation, and scale to your ML workflows. Over three days, you’ll explore the use of tools and processes to monitor, deploy, and automate machine learning pipelines using AWS-native services like Amazon SageMaker, CodePipeline, and CloudWatch.

This course builds upon DevOps best practices and software development principles to help you move from experimental models to reliable production systems. You'll also discuss the use of tools and teamwork in addressing the challenges associated with ML deployment and monitoring.

Course Objectives

By the end of the course, you’ll understand how to deploy machine learning models, take action when the model prediction in production drifts from agreed-upon key performance indicators, and automate the full ML lifecycle—from code to successful ML deployment in the AWS cloud.

Implement DevOps best practices in machine learning workflows
Design, deploy, and monitor secure, scalable ML pipelines
Use Amazon SageMaker for experimentation, tuning, and deployment
Automate CI/CD workflows for ML models, data, and code
Take action when the model prediction drifts from KPIs
Extend the DevOps approach to ML teams including data scientists, data engineers, and software developers

Who Should Attend?

This course is intended for any one of the following roles with responsibility for productionizing machine learning models in the AWS Cloud: DevOps engineers ML engineers Developers/operations with responsibility for operationalizing ML models

Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

What is MLOps Engineering on AWS?

It’s a 3-day, hands-on course that teaches professionals how to apply DevOps principles to machine learning workflows. You'll learn how to build, automate, and monitor ML pipelines in the AWS cloud.

Are MLOps engineers in demand?

Yes. With MLOps practices becoming more prevalent in software development, the demand for engineers who can automate and deploy machine learning models continues to rise.

How does this AWS training help with deployment and monitoring?

You’ll design and deploy full ML pipelines, implement best practices, and use tools like Amazon SageMaker Model Monitor to detect drift and automate retraining.

Does this course help me deploy machine learning models in production?

Absolutely. You’ll gain real-world experience with pipeline automation, deployment options, and model monitoring—all in the context of AWS-native tools.

Does this course support AWS certifications?

While it doesn’t map to a specific exam, it supports skills aligned with the AWS Certified Machine Learning – Specialty, DevOps Engineer – Professional, and Solutions Architect – Associate certifications.