8781  Reviews star_rate star_rate star_rate star_rate star_half
$2,495USD
Duration 4 days
Course Code AI-300T00
Available Formats Classroom, Virtual
Next Class: Dec 14, 2026

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Overview

Course Description

This course prepares learners to design, implement, and operate Machine Learning Operations (MLOps) and Generative AI Operations (GenAIOps) solutions on Azure. It covers building secure and scalable AI infrastructure, managing the full lifecycle of traditional machine learning models with Azure Machine Learning, and deploying, evaluating, monitoring, and optimizing generative AI applications and agents using Microsoft Foundry. Learners will gain hands-on knowledge of automation, continuous integration and delivery, infrastructure as code, and observability by using tools such as GitHub Actions, Azure CLI, and Bicep. The course emphasizes collaboration with data science and DevOps teams to deliver reliable, production-ready AI systems aligned with modern MLOps and GenAIOps best practices.

Audience Profile

This course is intended for data scientists, machine learning engineers, and DevOps professionals who want to design and operate production-grade AI solutions on Azure. It is suited for learners with experience in Python, a foundational understanding of machine learning concepts, and basic familiarity with DevOps practices such as source control, CI/CD, and command-line tools, who are preparing to implement MLOps and GenAIOps workflows using Azure-native services.

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Course Details

Course Details

Outline

  • Get started with machine learning in Azure
    • Define the problem
    • Get and prepare data
    • Train the model
    • Use Azure Machine Learning studio
    • Integrate a model
    • Module assessment
  • Experiment with Azure Machine Learning
    • 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
    • Module assessment
  • Run training scripts and track models with MLflow in Azure Machine Learning
    • Convert a notebook to a script
    • Run a script as a command job
    • Use parameters in a command job
    • Track metrics with MLflow
    • View metrics and evaluate models
    • Exercise - Optimize model training in Azure Machine Learning
    • Module assessment
  • Perform hyperparameter tuning with 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
    • Module assessment
  • Run pipelines in Azure Machine Learning
    • Create components
    • Create a pipeline
    • Run a pipeline job
    • Exercise - Run a pipeline job
    • Module assessment
  • Automate model training with GitHub Actions
    • Use source control for machine learning assets
    • Apply trunk-based development
    • Validate changes with GitHub Actions
    • Connect GitHub Actions to Azure Machine Learning securely
    • Trigger Azure Machine Learning jobs and pipelines
    • Exercise
    • Module assessment
  • Deploy and monitor a model in Azure Machine Learning
    • Prepare a model for deployment
    • Explore the deployment and monitoring architecture
    • Control deployments with GitHub environments
    • Deploy, promote, and roll back a model with GitHub Actions
    • Monitor the deployed model
    • Exercise
    • Module assessment
  • Plan and prepare a GenAIOps solution
    • Define agent specifications
    • Design and optimize your agent
    • Explore the GenAIOps lifecycle
    • Explore available tools and frameworks to implement GenAIOps
    • Exercise - Design agent specifications from team input
    • Module assessment
  • Manage prompts for agents in Microsoft Foundry with GitHub
    • 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
    • Knowledge check
  • Evaluate and optimize AI agents through structured experiments
    • Design evaluation experiments
    • Apply Git-based workflows to optimization experiments
    • Apply evaluation rubrics for consistent scoring
    • Exercise - Evaluate and compare AI agent versions
    • Knowledge check
  • Automate AI evaluations with Microsoft Foundry and GitHub Actions
    • 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
    • Knowledge check
  • Monitor your generative AI application
    • 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
    • Knowledge check
  • Analyze and debug your generative AI app with tracing
    • 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
    • Knowledge check

Schedule

3 options available

FAQ

How do I get a Microsoft exam voucher?

Pearson Vue Exam vouchers can be requested and ordered with your course purchase.

  • Vouchers are non-refundable and non-returnable. Vouchers expire 12 months from the date they are issued unless otherwise specified in the terms and conditions.
  • Voucher expiration dates cannot be extended. The exam must be taken by the expiration date printed on the voucher.

Do Microsoft courses come with post lab access?

Most Microsoft official courses will include post-lab access ranging from 30 to 180 calendar days after instructor led course delivery. A lab training key in class will be provided that can be leveraged to continue connecting to a remote lab environment for the individual course attendee.

Does the course schedule include a Lunchbreak?

Lunch is normally an hour-long after 3-3.5 hours of the class day.

What languages are used to deliver training?

Microsoft courses are conducted in English unless otherwise specified.

Reviews

The class was very vast paced however the teacher was very good at checking in on us while giving us time to complete the labs.

it was good and very informative. Instructure covered everything in detail.

Easy to work with. Learning material pdfs were able to be printed out in color which was very nice to write on.

I liked the pace of the course. I like that I have more than instance to use the lab.

Good training material and good instruction. More time needs to be provided for the lab work.