Whizlabs

Microsoft Foundry Essentials: RAG, Fine-Tuning and AI Agents

Whizlabs

Microsoft Foundry Essentials: RAG, Fine-Tuning and AI Agents

Whizlabs Instructor

Instructor: Whizlabs Instructor

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Beginner level

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8 hours to complete
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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace

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Recently updated!

August 2026

Assessments

8 assignments

Taught in English

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There are 3 modules in this course

In this section, you'll learn how to deploy, validate, and test fine-tuned AI models using Azure Machine Learning. You'll begin by preparing high-quality training datasets and importing them into Azure Machine Learning, establishing the foundation for successful model deployment and evaluation. As you progress, you'll explore foundation models available in Azure Machine Learning and learn how to select the most appropriate model based on your application requirements. You'll then deploy fine-tuned models to managed endpoints, gaining practical experience with the deployment workflow and understanding the key considerations for serving AI models in production environments. The section also focuses on validating deployed models by testing inference endpoints and evaluating model responses. You'll learn how to identify and troubleshoot common deployment and endpoint issues, ensuring that fine-tuned models perform reliably and meet application requirements. By the end of this section, you'll have a solid understanding of the end-to-end deployment process for fine-tuned models in Azure Machine Learning, from dataset preparation and model deployment to endpoint testing and troubleshooting, enabling you to confidently deploy and validate enterprise-ready AI solutions.

What's included

4 videos2 readings2 assignments

In this section, you'll build a strong foundation in Microsoft Foundry and learn how to develop, optimize, and evaluate generative AI models using Azure AI services. You'll begin by exploring the Microsoft Foundry Portal, including the Model Catalog, AI playgrounds, and development tools, to understand how Microsoft Foundry supports the end-to-end AI development lifecycle.As you progress, you'll explore Retrieval-Augmented Generation (RAG) and learn how it enhances generative AI applications by combining foundation models with external knowledge sources. You'll examine the differences between RAG and model fine-tuning, understand when to use each approach, and explore practical strategies for optimizing AI applications based on different business and technical requirements. The section further introduces model optimization techniques using Microsoft Foundry. You'll learn how to create AI playgrounds, fine-tune foundation and OpenAI models, and evaluate the performance of customized models through guided demonstrations. You'll also gain insight into AI evaluation, governance, and monitoring capabilities that help ensure models remain reliable, efficient, and ready for enterprise deployment.By the end of this section, you'll have a solid understanding of Microsoft Foundry's AI development capabilities, Retrieval-Augmented Generation (RAG), model fine-tuning workflows, and the tools required to build, optimize, and evaluate enterprise-ready generative AI solutions.

What's included

8 videos1 reading3 assignments1 discussion prompt

In this section, you'll learn how to organize AI development projects, manage resources, and build intelligent AI agents using Microsoft Foundry. You'll begin by exploring Microsoft Foundry hubs, projects, and the resources required to support AI development. You'll also learn about access control, project organization, quotas, and token management to effectively manage AI workloads within Microsoft Foundry. As you progress, you'll discover how Azure AI Search enhances generative AI applications by enabling intelligent search and retrieval capabilities. You'll learn how to create and configure a search index in the Azure portal, providing the foundation for building AI applications that leverage enterprise knowledge and Retrieval-Augmented Generation (RAG) scenarios. The section then focuses on building intelligent AI agents using Microsoft Foundry Agent Service. You'll gain hands-on experience creating and testing AI agents before integrating them into applications using the Microsoft Foundry SDK. Through guided demonstrations, you'll learn how to execute projects, configure agent workflows, and enable seamless communication between AI agents and external applications. By the end of this section, you'll have a solid understanding of Microsoft Foundry resource management, Azure AI Search integration, AI agent development, and SDK-based application integration, enabling you to build, deploy, and manage intelligent, enterprise-ready AI solutions.

What's included

8 videos2 readings3 assignments

Instructor

Whizlabs Instructor
Whizlabs
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