Data Science

Designing and Implementing a Microsoft Azure AI Solution

This course is focused on designing, developing, deploying, and managing AI solutions using Microsoft Azure AI services. It is closely aligned with the skills covered in Microsoft's Azure AI Engineer learning path and certification.

12 Lessons

4 Days Duration

Advanced Level

Azure AI services, AI solutions design, implementation, Azure OpenAI, cognitive services, security and integration Completion

Course Overview

Designing and Implementing a Microsoft Azure AI Solution

This course is focused on designing, developing, deploying, and managing AI solutions using Microsoft Azure AI services. It is closely aligned with the skills covered in Microsoft’s Azure AI Engineer learning path and certification.

1. Azure AI Fundamentals

Introduction to Artificial Intelligence
AI workloads and common use cases
Azure AI services overview
Azure AI Foundry
Azure resources and subscriptions
Azure Portal
Authentication and access management

2. Azure AI Foundry

Creating AI projects
Working with AI models
Model catalogs
Generative AI applications
Prompt engineering
Model evaluation
Deploying AI solutions
Monitoring AI applications

3. Generative AI & Azure OpenAI

Azure OpenAI Service
Large Language Models (LLMs)
GPT models
Prompt engineering
System, user and assistant messages
Temperature and token concepts
Chat applications
Text generation
Summarization
Translation
Question answering

4. Retrieval-Augmented Generation (RAG)

RAG architecture
Document ingestion
Chunking
Embeddings
Vector search
Azure AI Search
Grounding LLM responses with enterprise data
Building knowledge-based chatbots

5. Azure AI Search

Search services
Indexes and indexers
Data sources
Full-text search
Semantic search
Vector search
Hybrid search
Filtering and ranking

6. Computer Vision

Image analysis
Image classification
Object detection
Optical Character Recognition (OCR)
Face detection concepts
Image tagging
Azure AI Vision
Document Intelligence

7. Azure AI Document Intelligence

Extracting text from documents
Forms and invoices
Prebuilt models
Custom models
Tables and key-value pairs
Document classification
Automated document processing

8. Natural Language Processing (NLP)

Text analysis
Sentiment analysis
Key phrase extraction
Named Entity Recognition (NER)
Language detection
Text classification
Question answering
Azure AI Language

9. Speech AI

Speech-to-Text
Text-to-Speech
Speech translation
Voice-enabled applications
Custom speech concepts
Azure AI Speech

10. AI Agents

AI agents
Agent architecture
Tools and actions
Grounding agents with enterprise data
Multi-step reasoning workflows
Agent evaluation
Building intelligent assistants

11. Responsible AI

AI safety
Content filtering
Responsible AI principles
Privacy
Security
Fairness
Transparency
Reliability
Human oversight

12. Security & Identity

Microsoft Entra ID
Managed identities
Azure RBAC
Key Vault
API authentication
Network security
Secure AI endpoints
Data protection

13. AI Application Development

Python basics for AI
Azure SDK
REST APIs
Azure AI client libraries
Connecting applications to AI services
Error handling
Logging and monitoring

14. AI Solution Architecture

Designing scalable AI solutions
Selecting appropriate Azure AI services
Cost considerations
Performance optimization
Availability and reliability
Security architecture
Data flow and integration

15. Deployment & Monitoring

Deploying AI applications
Azure App Service
Azure Functions
Containers
CI/CD concepts
Application monitoring
AI model evaluation
Performance monitoring
Cost monitoring

Real-World Projects

Project 1 – AI Chatbot

Azure OpenAI
RAG
Azure AI Search
Enterprise documents
Conversational interface

Project 2 – Invoice Processing System

Azure AI Document Intelligence
OCR
Structured data extraction
Database integration

Project 3 – Customer Sentiment Analysis

Azure AI Language
Sentiment analysis
Entity extraction
Dashboard/reporting

Project 4 – AI Agent

Azure AI Foundry
LLM
Tools
Enterprise knowledge
Secure deployment

Key Skills