Operationalize Machine Learning and Generative AI Solutions (AI-300) Course

Course Code: AI-300

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Duration: 4 days

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Course Overview

This course focuses on operationalising machine learning and generative AI solutions on Microsoft Azure. It covers the secure, scalable infrastructure and automated processes required to deploy, monitor, evaluate and improve production AI systems.

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Target Audience

This course is intended for data scientists, machine learning engineers and DevOps professionals who want to implement production-grade MLOps and GenAIOps workflows using Azure-native services.

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Course Objectives

• Design and operate secure, scalable AI infrastructure.
• Manage the machine learning model lifecycle with Azure Machine Learning.
• Automate AI deployment using GitHub Actions and infrastructure as code.
• Deploy, evaluate and monitor generative AI applications and agents.
• Use tracing and observability to troubleshoot production AI systems.

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Course Content

Operationalise Machine Learning Models

• Experiments, hyperparameter tuning and pipelines in Azure Machine Learning.
• GitHub Actions, feature-based development, environments and model deployment.

Operationalise Generative AI Applications

• Plan and prepare a GenAIOps solution.
• Manage prompts and agents with Microsoft Foundry and GitHub.
• Evaluate, optimise, monitor, trace and debug AI applications.

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Course Prerequisites

A basic understanding of machine learning, Python and DevOps practices is recommended, together with familiarity with Azure Machine Learning, source control, CI/CD and command-line tools.

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Note - This course is delivered via our training partner Skillsoft Global Knowledge.