15 reviews

Microsoft Machine Learning Operations (MLOps) Engineer (AI-300)

Item: microsoft-machine-learning-operations-mlops-engineer-ai-300-course
Rating: 10
Author: Stephen Ridgway

The Microsoft Machine Learning Operations (MLOps) Engineer (AI-300) course equips professionals with the skills needed to operationalize machine learning models at scale. It focuses on bridging the gap between data science and production by implementing robust MLOps practices using Microsoft Azure tools and services. Participants will learn how to build, deploy, monitor, and manage machine learning workflows efficiently while ensuring reliability, scalability, and governance across the ML lifecycle.

Course: Microsoft Machine Learning Operations (MLOps) Engineer (AI-300)

Duration: 4 days

Format: Virtual or Classroom

Prepares for Exam: Operationalizing Machine Learning and Generative AI Solutions AI-300

Prepares for Certification: Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate

Attend this and 60+ other Microsoft courses for FREE with Unlimited Microsoft Training

Overview

The Microsoft Machine Learning Operations (MLOps) Engineer (AI-300) course provides a comprehensive understanding of how to manage the end-to-end lifecycle of machine learning models in production environments. Covering topics such as model versioning, CI/CD pipelines, monitoring, and governance, the course emphasizes practical implementation using Azure Machine Learning and related services. Learners gain hands-on experience in automating workflows, managing model performance, and ensuring operational excellence. This course prepares candidates to take the AI-300 certification exam and validates their ability to design and implement MLOps solutions effectively.

This course includes

Instructor-led training
Personal Learning Path
Email, chat and phone support
Certification Guarantee

Who is the Microsoft Machine Learning Operations (MLOps) Engineer (AI-300) course for?

This course is ideal for data scientists, machine learning engineers, DevOps professionals, and AI practitioners who want to operationalise machine learning solutions. It is also suitable for professionals responsible for deploying and maintaining ML models in production environments, as well as those looking to validate their skills with the AI-300 certification.

Curriculum

What you will learn during our Microsoft Machine Learning Operations (MLOps) Engineer (AI-300) course.

Experiment with Azure Machine Learning

Learn how to find the best machine learning model with automated machine learning (AutoML), MLflowtracked notebooks, and the Responsible AI dashboard.
Preprocess data and configure featurization
Run an automated machine learning experiment
Evaluate and compare models
Configure MLflow for model tracking in notebooks
Train and track models in notebooks
Evaluate models with the Responsible AI dashboard
Exercise Find the best classification model with Azure Machine Learning

Perform hyperparameter tuning with Azure Machine Learning

Learn how to perform hyperparameter tuning with a sweep job in Azure Machine Learning.
Define a search space
Configure a sampling method
Configure early termination
Use a sweep job for hyperparameter tuning
Exercise Run a sweep job

Run pipelines in Azure Machine Learning

Learn how to create and use components to build pipeline in Azure Machine Learning. Run and schedule Azure Machine Learning pipelines to automate machine learning workflows.
Create components
Create a pipeline
Run a pipeline job
Exercise Run a pipeline job

Trigger Azure Machine Learning jobs with GitHub Actions

Learn how to automate your machine learning workflows by using GitHub Actions.
Understand the business problem
Explore the solution architecture
Use GitHub Actions for model training
Exercise

Trigger GitHub Actions with feature-based development

Learn how to protect your main branch and how to trigger tasks in the machine learning workflow based on changes to the code.
Understand the business problem
Explore the solution architecture
Trigger a workflow
Exercise

Work with environments in GitHub Actions

Learn how to train, test, and deploy a machine learning model by using environments as part of your machine learning operations (MLOps) strategy.
Understand the business problem
Explore the solution architecture
Set up environments
Exercise

Deploy a model with GitHub Actions

Learn how to automate and test model deployment with GitHub Actions and the Azure Machine Learning CLI (v2).
Understand the business problem
Explore the solution architecture
Model deployment
Exercise

Plan and prepare a GenAIOps solution

Learn how to develop chat applications with language models using a codefirst development approach. By developing generative AI apps codefirst, you can create robust and reproducible flows that are integral for generative AI Operations, or GenAIOps.
Explore use cases for GenAIOps
Select the right generative AI model
Understand the development lifecycle of a language model application
Explore available tools and frameworks to implement GenAIOps
Exercise Compare language models from the model catalog

Manage prompts for agents in Microsoft Foundry with GitHub

Learn how to manage AI prompts as versioned assets using GitHub. Apply software engineering best practices to create, test, and promote prompt versions used in Microsoft Foundry as part of a GenAIOps workflow.
Apply version control to prompts
Understand Microsoft Foundry agents and prompt versioning
Organize prompts in GitHub repositories
Develop safe prompt deployment workflows
Exercise Develop prompt and agent versions

Evaluate and optimize AI agents through structured experiments

Learn how to optimize AI agents through structured evaluation that transforms guesswork into evidencebased engineering decisions. You'll explore how to design evaluation experiments with clear metrics for quality, cost, and performance; organize experiments using Gitbased workflows; create evaluation rubrics for consistent scoring; and compare results to make informed optimization decisions.
Design evaluation experiments
Apply Gitbased workflows to optimization experiments
Apply evaluation rubrics for consistent scoring
Exercise Evaluate and compare AI agent versions

Automate AI evaluations with Microsoft Foundry and GitHub Actions

Learn how to implement automated evaluations for AI agent responses using Microsoft Foundry evaluators, create evaluation datasets from production data and synthetic generation, run batch evaluations with Python scripts, and integrate evaluation workflows into GitHub Actions for continuous quality assurance.
Understand why automated evaluations matter
Align evaluators with human criteria
Create evaluation datasets
Implement batch evaluations with Python
Integrate evaluations into GitHub Actions
Exercise Set up automated evaluations

Monitor your generative AI application

Learn how to monitor the performance of your generative AI application using Microsoft Foundry. This module teaches you to track key metrics like latency and token usage to make informed, costeffective deployment decisions.
Why do you need to monitor?
Understand key metrics to monitor
Explore how to monitor with Azure
Integrate monitoring into your app
Interpret monitoring results
Exercise Enable monitoring for a generative AI application

Analyze and debug your generative AI app with tracing

Learn how to implement tracing in your generative AI applications using Microsoft Foundry and OpenTelemetry. This module teaches you to capture detailed execution flows, debug complex workflows, and understand application behavior for better reliability and optimization.
Why do you need to use tracing?
Identify what to trace in generative AI applications
Implement tracing in generative AI applications
Debug complex workflows with advanced tracing patterns
Make informed decisions with trace data analysis
Exercise Enable tracing for a generative AI application

Preparation

At Readynez, we provide many resources and have experienced experts in the field. That is why we are also very successful with many satisfied customers. You can therefore safely take your course with us. While there are no strict entry requirements, certain foundational knowledge may enhance your learning experience. Any recommended prerequisites specific to this course are detailed below.

Basic understanding of machine learning concepts and workflows
Familiarity with Python and data science tools
Knowledge of cloud computing fundamentals, preferably Microsoft Azure
Understanding of DevOps principles such as CI/CD pipelines
Experience working with data models or ML projects is beneficial

Meet our instructors

Meet some of the Readynez Instructors you can meet on your course. They are experts, passionate about what they do, and dedicated to give back to their industry, their field, and those who want to learn, explore, and advance in their careers.

Tiago Costa

With a long-standing reputation as a Microsoft MVP and MCT, Tiago Costa delivers LIVE training that blends expert knowledge with real-world application. His courses help professionals build hands-on cloud skills and prepare effectively for Microsoft certifications.

Meet the Instructor: Tiago Costa

At Readynez, we believe world-class training starts with world-class instructors - and Tiago Costa is a prime example.

With over 20 years of experience delivering Microsoft Cloud & AI solutions (including to Fortune 500 companies), Tiago brings a rare combination of deep technical expertise and real-world perspective to every course.

50+ Microsoft certifications.
Microsoft MVP since 2016 & Microsoft Certified Trainer since 2006. 
Speaker, architect, and trusted advisor to global enterprises.

As a dedicated community leader, an independent contractor and founder of the Azure & AI Portugal User Group, Tiago is passionate about helping others grow their careers through knowledge sharing and practical skills.

When you train with Tiago, you’re not just learning from certified professionals...
You’re learning from a global leader.

Find your next Microsoft course with Tiago Costa here.

FAQs

FAQs for the Microsoft Machine Learning Operations (MLOps) Engineer (AI-300) course.

What is Microsoft Machine Learning Operations (MLOps) Engineer (AI-300)?

This course focuses on implementing MLOps practices using Microsoft Azure. It teaches how to build, deploy, monitor, and manage machine learning models in production and prepares candidates for the AI-300 certification exam.

How do I prepare for the Microsoft Machine Learning Operations (MLOps) Engineer (AI-300) course?

Preparation includes understanding basic machine learning workflows, Azure fundamentals, and DevOps concepts. Reviewing Python and data science practices will also help maximize your learning.

What are the prerequisites for Microsoft Machine Learning Operations (MLOps) Engineer (AI-300)?

There are no strict prerequisites, but knowledge of machine learning, cloud computing, and DevOps practices is highly recommended.

How much does the Microsoft Machine Learning Operations (MLOps) Engineer (AI-300) course cost?

The cost of Microsoft Machine Learning Operations (MLOps) Engineer course is €2,890.

What topics are covered in the Microsoft Machine Learning Operations (MLOps) Engineer (AI-300) course?

Topics include model lifecycle management, CI/CD pipelines for ML, model monitoring, versioning, governance, Azure Machine Learning, and automation of ML workflows.

Is Microsoft Machine Learning Operations (MLOps) Engineer (AI-300) certification worth it for my career?

Yes, it validates your ability to operationalize machine learning solutions—an increasingly in-demand skill across industries adopting AI at scale.

How long does it take to complete the Microsoft Machine Learning Operations (MLOps) Engineer (AI-300) course?

The course is typically delivered as a short, intensive training program designed to prepare candidates efficiently for the certification exam.

Can I take the Microsoft Machine Learning Operations (MLOps) Engineer (AI-300) course online?

Yes, the course is available both online and in instructor-led formats with hands-on labs.

How easy or difficult is it to complete the Microsoft Machine Learning Operations (MLOps) Engineer (AI-300) course?

The course can be moderately challenging, especially for those new to MLOps, but hands-on labs and structured learning make it manageable.

What is the passing score for the Microsoft Machine Learning Operations (MLOps) Engineer (AI-300) course?

Passing requires successfully completing the AI-300 certification exam. The exact passing score is determined by the exam provider and may vary.

How can I maintain my Microsoft Machine Learning Operations (MLOps) Engineer (AI-300) knowledge?

Stay updated with Azure ML tools, practice deploying models, and continuously work with CI/CD pipelines and monitoring solutions in real-world environments.

How much salary can I get after completing Microsoft Machine Learning Operations (MLOps) Engineer (AI-300)?

Professionals with MLOps expertise typically earn between €70,000 and €130,000 annually, depending on experience, location, and role.

Reviews

Feedback from our delegates.

Johan Andersson

Easy to attend over Teams and an excellent instructor gave me great value for the time I invested.

Stephen Ridgway

Readynez is the best training provider I've used for many years. Their customer service is first class, prices are very competitive and instruction excellent.