This course provides an in-depth guide to utilizing Azure OpenAI for building scalable, intelligent AI solutions. By covering foundational concepts, best practices, and real-world applications, learners will understand how to integrate Azure OpenAI with various enterprise systems, ensuring high performance and security.
You’ll gain hands-on experience in deploying generative AI models, fine-tuning them, and operationalizing them for real-world applications. Through practical exercises, you’ll also work on solving complex problems like document question-answer solutions, contact center analytics, and multimodal agent frameworks.
What makes this course unique is the combination of theoretical foundations with practical examples, such as transforming text to video and building code generation solutions. Each module is designed to equip you with essential skills for creating cutting-edge AI-powered solutions using Azure.
This course is ideal for software developers, data scientists, AI engineers, and IT professionals with basic Python knowledge and an Azure subscription. You’ll leave with the expertise to build enterprise-grade solutions using Azure OpenAI and transform AI capabilities for your organization.
- Introduction to Large Language Models
This module introduces learners to the fundamentals of large language models (LLMs), highlighting their rapid adoption and transformative impact on technology. Learners will explore leading LLM examples from major companies and discover diverse real-world applications across industries. By the end, participants will understand the capabilities and significance of LLMs in today's AI landscape.
- Azure OpenAI Fundamentals
This module introduces learners to the essentials of working with Azure OpenAI, including setting up resources, navigating the Azure AI Foundry, and making programmatic calls using Python. By the end, you will understand how to deploy and interact with large language models on Azure's platform.
- Azure OpenAI Advanced Topics
This module explores advanced features of Azure OpenAI, including vector databases, custom data integration, fine-tuning, and the use of APIs for building intelligent assistants. Learners will gain practical skills in optimizing AI models, managing data privacy, and leveraging batch processing and code interpretation tools. Emphasis is placed on best practices for data quality and secure deployment in enterprise environments.
- Developing an Enterprise Document Question-Answer Solution
This module guides learners through the foundational steps of building a document-based question-answering solution using enterprise tools. You will explore key prerequisites, including Azure subscriptions and OpenAI resources, and learn how to set up Azure Cognitive Search for effective information retrieval.
- Building Contact Center Analytics
This module introduces strategies for organizing and searching scattered contact center documents using Azure OpenAI. Learners will discover how to set up tools in the Azure portal and leverage Python to create a searchable document repository. By the end, you'll understand how to streamline information retrieval for contact center analytics.
- Querying from a Structured Database
This module introduces learners to the fundamentals of retrieving information from structured SQL databases. You will explore how to access and query stored data, building on previous work with customer conversation analysis. By the end, you'll be able to extract meaningful insights from organized datasets.
- Code Generation and Documentation
This module explores the process of generating code and creating effective documentation, building on your experience with Azure Communication Services and synthetic chat scenarios. Learners will discover best practices for automating code creation and documenting their solutions for clarity and maintainability.
- Creating a Basic Recommender Solution with Azure OpenAI
This module introduces learners to building a foundational recommender system using Azure OpenAI. You will explore how to leverage AI tools to generate code snippets and accompanying documentation, setting the stage for more advanced AI-powered solutions.
- Transforming Text to Video
This module guides learners through the process of converting educational text content into engaging video presentations using Azure OpenAI and Azure Language and Speech Services. Participants will gain hands-on experience setting up the necessary cloud services and developing Python-based solutions for automated video creation.
- Creating a Multimodal Multi-Agent Framework with the Azure OpenAI Assistant API
This module guides learners through building a collaborative system of intelligent agents using the Azure OpenAI Assistants API. You will explore architectural design, agent communication, and the integration of multimodal capabilities such as image generation and analysis. By the end, you'll understand how to orchestrate multiple agents to perform complex tasks autonomously.
- Privacy and Security
This module explores essential privacy and security practices when deploying Azure OpenAI, including content filtering, abuse prevention, and responsible AI implementation. Learners will gain hands-on experience configuring security features, testing private endpoints, and understanding risk mitigation strategies for advanced language models.
- Operationalizing Azure OpenAI
This module guides learners through the practical aspects of deploying, monitoring, and scaling Azure OpenAI services. You will learn how to generate and analyze operational logs, accurately size resources using PTU-M, and manage service quotas for optimal performance. By the end, you'll be equipped to operationalize Azure OpenAI in real-world scenarios.
- Advanced Prompt Engineering
This module explores advanced techniques for crafting effective prompts for large language models, including strategies for structuring inputs, decomposing complex tasks, and optimizing outputs. Learners will also examine the differences between prompt engineering and fine-tuning, as well as methods to mitigate prompt injection attacks. Practical examples and best practices are provided to enhance model performance and security.
