Highlights
- What is Artificial Intelligence? Definitions of Artificial Intelligence
- Ready-to-Use AI Models with Azure AI Services
- Azure AI Services Overview, Azure AI Language & Azure AI Vision
- Orchestrating AI Models using Semantic Kernel
- Retrieving Semantically Related Data with Vector Search
- Using your own data in a LLM with Azure AI Search
- Prompt Engineering and Design Patterns
- Deploying AI Models on Azure AI Foundry
- Testing and Moderating AI Models
- Building on the Microsoft Copilot Ecosystem
Course Details
What is Artificial Intelligence?
In this chapter you will get a short overview about what AI is exactly, and what we can do with it.
- Definitions of Artificial Intelligence
- Machine Learning Basics
- Domains of Artificial Intelligence
- History, Current State and Future
Ready-to-Use AI Models with Azure AI Services
Azure AI services provides a comprehensive suite of out-of-the-box and customizable AI tools, APIs, and pre-trained models that detect sentiment, recognize speakers, understand pictures and many more.
- Azure AI Services Overview
- Azure AI Language
- Azure AI Vision
- Azure AI Speech
- Azure AI Document Intelligence
Azure OpenAI and Large Language Model Fundamentals
This module introduces Azure OpenAI and the GPT family of Large Language Models (LLMs). You'll learn about available LLM models, how to configure and use them in the Azure Portal, and the Transformer architecture behind models like GPT-4o. The latest GPT models offer Function Calling, enabling connections to external tools, services, or code, allowing the creation of AI-powered Copilots. Additionally, you'll discover how Azure OpenAI provides a secure way to use LLMs without exposing your company's private data.
- Introducing OpenAI and Large Language Models
- The Transformer Model
- What is Azure OpenAI?
- Configuring Deployments
- Understanding Tokens
- LLM Pricing
- Azure OpenAI Chat Completions API
- Role Management: System, User and Assistant
- Azure OpenAI SDK
- Extending LLM capabilities with Function Calling
- LAB: Deploying and Using Azure OpenAI
Orchestrating AI Models using Semantic Kernel
- An Introduction to Semantic Kernel
- Integrating LLMs in your applications
- Keeping track of Token Usage
- Enable AI Models to execute code using Plugins
- Control AI Models with Filters
- Best practices for dependency injection in managing AI services
- Observable AI Apps with OpenTelemetry
- LAB: Create a Natural Language to SQL Translation Copilot
Retrieving Semantically Related Data with Vector Search
Vector search is a powerful technique that allows you to retrieve semantically related data from large datasets such as company documents or databases.
This chapter will teach you how vector search works and how it enables you to find relevant information without depending on exact keyword based search terms or language of the information in the dataset.
- Capture Semantic Meaning with Embeddings
- Vector Search
- Vector Search Design Considerations
Using your own data in a LLM with Azure AI Search
Azure AI Search facilitates the adoption of the Retrieval Augmented Generation (RAG) design pattern.
This methodology involves retrieving pertinent information from a data source and using it to increase the knowledge of generative AI models.This combination of retrieval and generation sets a new standard for AI-driven search solutions.
- What is Azure AI Search?
- Retrieval Augmented Generation
- Creating an Index on your Own Data
- AI Enrichment with your own Data
- Using the Azure OpenAI SDK
- Privacy Concerns
- Fine-tuning vs RAG
- LAB: Chat with Azure OpenAI models using your own data
Prompt Engineering and Design Patterns
In this chapter, you'll explore advanced techniques allowing you to control the model's output, transforming generic responses into precise, valuable results.
Additionally the chapter covers emerging design patterns in the field of Gen AI app development that help you increase quality of model responses and reduce costs.
- What is Prompt Engineering?
- Few-Shot Prompting
- Structured Query Generation
- Verifying Model responses with Hallucination Detection
- Saving costs with Semantic Caching
Deploying AI Models on Azure AI Foundry
Learn about the available model catalog, featuring state-of-the-art Azure OpenAI models and open-source models from Hugging Face, Meta, Google, Microsoft, Mistral, and many more.
- Model Catalog Overview
- Model Benchmarks
- Selecting the Best Deployment Mode
Working with Open-Source Language Models
This chapter empowers you to bring powerful AI capabilities to end-user environments like mobile devices, personal computers and browsers, enhancing scalability, costs and performance. Additionally you will learn how to deploy and host your own open-source Language Models in the form of an API that you have full control over.
- The Phi-3 Family of Small Language Models
- Deploying AI Models on Mobile and Edge Devices with ONNX Runtime
- Hosting and Deploying Language Models on-prem and in the cloud with Ollama
Testing and Moderating AI Models
How can you ensure an LLM provides relevant and coherent answers to users' questions using the correct info? How do you prevent an LLM from responding inappropriately? Discover the answers to these questions and more by exploring evaluation metrics in Azure AI Foundry and the Azure AI Content Safety Service.
- Ensuring Coherent and Relevant LLM Responses
- Utilizing Correct Information in AI Answers
- Preventing Inappropriate LLM Responses
- Leveraging Azure AI Content Safety Service
- Enhancing AI Performance and Safety
Building on the Microsoft Copilot Ecosystem
While building a complete AI-powered application from scratch can be beneficial, it is sometimes not the most efficient approach. In this chapter, you will learn the basics of extending the capabilities and knowledge of Copilot for Microsoft 365, allowing you to enhance its functionality and leveraging its robust and secure infrastructure and UI.
- Overview of Copilot for Microsoft 365 Extensibility Options
- Overview of Copilot Studio
- Extending Copilot's knowledge with Graph Connectors
- Allowing Microsoft Copilot to call REST API's
- Expanding Copilot Capabilities via Teams Message Extensions
Who should attend
This course targets professional C# developers that want to get started with the Microsoft AI platform. Participants of this course need to have a decent understanding of C# and preferably some experience with Microsoft Azure.
This is not a course for data scientists who want to build their own AI models or understand how existing AI models work.
Feedback
4.8 out of 5 average
"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. " Brian Leek, Data Analyst, May 2022
“JBI did a great job of customizing their syllabus to suit our business. Our teams varied widely in terms of experience and the Instructor handled this particularly well - very impressive” Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2024