AI-300T00: Operationalize machine learning and generative AI solutions

Price
$2,495.00 USD

Duration
4 Days

 

Delivery Methods
Virtual Instructor Led
Private Group

Course Overview

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.

Who Should Attend?

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.
  • 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.

Learning Credits: Learning Credits can be purchased well in advance of your training date to avoid having to commit to specific courses or dates. Learning Credits allow you to secure your training budget for an entire year while eliminating the administrative headache of paying for individual classes. They can also be redeemed for a full year from the date of purchase. If you have previously purchased a Learning Credit agreement with New Horizons, you may use a portion of your agreement to pay for this class.

If you have questions about Learning Credits, please contact your Account Manager.

Corporate Tech Pass: Our Corporate Tech Pass includes unlimited attendance for a single person, in the following Virtual Instructor Led course types: Microsoft Office, Microsoft Technical, CompTIA, Project Management, SharePoint, ITIL, Certified Ethical Hacker, Certified Hacking Forensics Investigator, Java, Professional Development Courses and more. The full list of eligible course titles can be found at https://www.newhorizons.com/eligible.

If you have questions about our Corporate Tech Pass, please contact your Account Manager.

Agenda

1 - 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
  • Module assessment

2 - 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
  • Module assessment

3 - Run pipelines in Azure Machine Learning

  • Create components
  • Create a pipeline
  • Run a pipeline job
  • Module assessment

4 - Trigger Azure Machine Learning jobs with GitHub Actions

  • Understand the business problem
  • Explore the solution architecture
  • Use GitHub Actions for model training
  • Module assessment

5 - Trigger GitHub Actions with feature-based development

  • Understand the business problem
  • Explore the solution architecture
  • Trigger a workflow
  • Module assessment

6 - Work with environments in GitHub Actions

  • Understand the business problem
  • Explore the solution architecture
  • Set up environments
  • Module assessment

7 - Deploy a model with GitHub Actions

  • Understand the business problem
  • Explore the solution architecture
  • Model deployment
  • Module assessment

8 - Plan and prepare a GenAIOps solution

  • 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
  • Module assessment

9 - 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

10 - Evaluate and optimize AI agents through structured experiments

  • Design evaluation experiments
  • Apply Git-based workflows to optimization experiments
  • Apply evaluation rubrics for consistent scoring

11 - 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

12 - 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

13 - 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
 

Upcoming Class Dates and Times

Jul 20,21,22,23
8:00 AM - 4:00 PM
ENROLL $2,495.00 USD
Aug 17,18,19,20
8:00 AM - 4:00 PM
ENROLL $2,495.00 USD
Jan 18,19,20,21, 2027
8:00 AM - 4:00 PM
ENROLL $2,495.00 USD
CourseID: 3606833E
 



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