Azure MLOps and GenAIOps training to design, deploy, and operate production-ready AI and machine learning solutions using Azure ML and Microsoft Foundry.

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Duration: 4 days
Regular price: $3,495
Preferential price: $2,970

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Course outline

Duration : 4 Days

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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	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.
Prerequisites	Data scientists, machine learning engineers and technical professonals responsible for deploying, automating and operating machine learning and generative AI solutions on Azure.
Objectives	Design and run machine learning experiments using Azure Machine Learning, including AutoML Preprocess data, configure featurization, and evaluate, compare, and track models using MLflow and the Responsible AI dashboard Optimize models through hyperparameter tuning and the use of sweep jobs Design, run, and automate machine learning pipelines in Azure Machine Learning Apply MLOps best practices by automating training, evaluation, and deployment with GitHub Actions Deploy machine learning models in controlled, reproducible environments Plan and implement a GenAIOps approach for generative AI applications Select and compare language models based on real-world use cases Manage, version, and safely deploy prompts and AI agents using Microsoft Foundry and GitHub Evaluate, compare, and optimize generative AI agents using structured and automated experiments Implement automated AI evaluations aligned with human-defined criteria Monitor, analyze, and improve generative AI applications using monitoring and advanced tracing Diagnose and debug complex generative AI workflows using trace data
Teaching method	Training delivered by a Microsoft Certified Trainer (MCT)
Contents	Experiment with Azure Machine Learning Preprocess data and configure featurization Run an automated machine learning (AutoML) experiment Evaluate and compare models Configure MLflow for model tracking in notebooks Train and track models in notebooks Evaluate models using the Responsible AI dashboard Exercise – Find the best classification model with 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 Trigger Azure Machine Learning jobs with GitHub Actions Understand the business problem Explore the solution architecture Use GitHub Actions for model training Exercise Module assessment Trigger GitHub Actions with feature-based development Understand the business problem Explore the solution architecture Trigger a workflow Exercise Module assessment Work with environments in GitHub Actions Understand the business problem Explore the solution architecture Set up environments Exercise Module assessment Deploy a model with GitHub Actions Understand the business problem Explore the solution architecture Model deployment Exercise Module assessment Plan and prepare a GenAIOps solution Explore GenAIOps use cases 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 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 application Interpret monitoring results Exercise – Enable monitoring for a generative AI application Knowledge check Analyze and debug your generative AI application with tracing Why do you need 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 through trace data analysis Exercise – Enable tracing for a generative AI application Knowledge check

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Operationalize machine learning and generative AI solutions (AI-300T00)

Upcoming sessions

Course outline