Operationalizing Machine Learning and General AI Solutions (AI-300)
MLOps for Generative AI on the Azure Platform – The Official Microsoft Training
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Machine Learning Engineer
1. Experimenting with Azure Machine Learning
Learn how to find the best machine learning model using automated machine learning (AutoML), MLflow-tracked notebooks, and the Responsible AI dashboard.
2. Perform hyperparameter tuning with Azure Machine Learning
Learn how to perform hyperparameter tuning using a sweep job in Azure Machine Learning.
3. Run pipelines in Azure Machine Learning
Learn how to create and use components to build pipelines in Azure Machine Learning. Run and schedule Azure Machine Learning pipelines to automate machine learning workflows.
4. Trigger Azure Machine Learning jobs using GitHub Actions
Learn how to automate your machine learning workflows using GitHub Actions.
5. Triggering GitHub Actions with feature-based development
Learn how to protect your main branch and trigger tasks in the machine learning workflow based on changes to the code.
6. Working with Environments in GitHub Actions
Learn how to train, test, and deploy a machine learning model by using environments as part of your MLOps strategy.
7. Deploying a model using GitHub Actions
Learn how to automate and test model deployment using GitHub Actions and the Azure Machine Learning CLI (v2).
8. Planning and Preparing a GenAIOps Solution
Learn how to develop chat applications with language models using a code-first development approach. By developing generative AI apps using a code-first approach, you can create robust and reproducible workflows that are essential for generative AI operations (GenAIOps).
9. Managing Prompts for Agents in Microsoft Foundry Using GitHub
Learn how to manage AI prompts as versioned assets using GitHub. Apply software development best practices to create, test, and release prompt versions that are used in Microsoft Foundry as part of a GenAIOps workflow.
10. Evaluating and Optimizing AI Agents Through Structured Experiments
Learn how to optimize AI agents through structured evaluation and turn assumptions into evidence-based technical decisions. You’ll learn how to design evaluation experiments with clear metrics for quality, cost, and performance; organize experiments using Git-based workflows; create evaluation rubrics for consistent assessment; and compare results to make informed optimization decisions.
11. Automating AI Evaluations with Microsoft Foundry and GitHub Actions
Learn how to use Microsoft Foundry evaluators to implement automated evaluations of AI agent responses, create evaluation datasets from production data and synthetically generated data, run batch evaluations using Python scripts, and integrate evaluation workflows into GitHub Actions for continuous quality assurance.
12. Monitoring Your Generative AI Application
Learn how to monitor the performance of your generative AI application with Microsoft Foundry. In this module, you'll learn how to track key metrics such as latency and token consumption to make informed and cost-effective deployment decisions.
13. Analyzing and Debugging Your Generative AI App with Tracing
Learn how to implement tracing in your generative AI applications using Microsoft Foundry and OpenTelemetry. In this module, you'll learn how to capture detailed execution traces, debug complex workflows, and understand application behavior to improve reliability and optimize your application.
Requirements:
- Programming experience with Python or R
- Experience in developing and training machine learning models
- Familiarity with the basic concepts of Azure Machine Learning
- Familiarity with Git workflows for version control
- Experience with Microsoft Azure AI Foundry or similar AI development platforms
- Manage the lifecycle of ML models using AutoML, MLflow tracking, and the Responsible AI dashboard
- Optimize model performance by defining a search space for hyperparameters and running sweep jobs
- Automate MLOps workflows by creating Azure ML pipelines and securing them with service principals and GitHub Actions
- Enforce robust deployment practices across all GitHub environments using feature-based development, branch protection, and approval steps
- Understand the fundamentals of generative AI, including use cases, model selection, and GenAIOps for application lifecycle management
- Apply version control to prompts as code assets and set up GitHub repositories for secure testing and deployment
- Design GenAI evaluation experiments using clear metrics, Git-based workflows, and rubrics for automated and manual quality assurance
- Monitor GenAI applications for production readiness, track key metrics (latency, token consumption), and set up tracing to identify performance bottlenecks
This course consists of antraining is led by an instructor who provides live guidance to participants. Theory and practice are taught through live demonstrations and hands-on exercises. The course uses the video conferencing software Zoom.
This course is intended for data scientists, machine learningengineers , and DevOpsexperts who want to design and operate production-ready AI solutions on Azure.
This course is designed for participants with Python experience, a basic understanding of machine learning concepts, and a basic familiarity with DevOps practices such as source code management, CI/CD, and command-line tools, who are preparing to implement MLOps and GenAIOps workflows using Azure-native services.
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