Designing and Implementing a Microsoft Azure AI Solution (AI 102)

The AI-102 course equips learners with the skills to design and implement AI solutions using Microsoft Azure. This course focuses on leveraging Azure Cognitive Services, Azure OpenAI, and AI tools to build intelligent applications including chatbots, vision solutions, language processing, and more.

This course is designed for developers and AI engineers who want to build, manage, and deploy AI-powered applications on Azure. Participants will gain hands-on experience with Azure AI services such as Computer Vision, Language AI, Speech, and Azure OpenAI.

The course covers real-world scenarios including chatbot development, document intelligence, and conversational AI solutions.

Module 1: Introduction to AI on Azure

Topics Covered:

• Overview of AI workloads
• Azure AI services ecosystem
• Responsible AI principles

Lab:

• Create an Azure account and set up AI resources
• Explore Azure portal for AI services
• Deploy a basic AI service instance

Outcome:
Understand how to navigate Azure and provision AI services.

Module 2: Develop AI Solutions with Azure OpenAI

Topics Covered:

• Generative AI fundamentals
• Prompt engineering techniques
• Azure OpenAI models and APIs

Lab:

• Create an Azure OpenAI resource
• Build a text generation app using prompts
• Fine-tune responses using prompt design

Outcome:
Build and customize generative AI applications.

Module 3: Computer Vision Solutions

Topics Covered:

• Image analysis and tagging
• Object detection
• Face detection and recognition

Lab:

• Analyze images using Vision Studio
• Build an object detection application
• Integrate vision APIs into an app

Outcome:
Develop applications that can “see” and interpret images.

Module 4: Natural Language Processing (NLP)

Topics Covered:

• Text analytics (sentiment, key phrases)
• Language understanding
• Named entity recognition

Lab:

• Perform sentiment analysis on text data
• Extract key phrases and entities
• Build a simple language processing API

Outcome:
Create intelligent text-based solutions.

Module 5: Speech Services

Topics Covered:

• Speech-to-text and text-to-speech
• Speech translation
• Voice-enabled applications

Lab:

• Convert speech to text in real time
• Generate speech from text
• Build a voice-enabled application

Outcome:
Develop applications that can listen and speak.

Module 6: Conversational AI Solutions

Topics Covered:

• Bot Framework fundamentals
• Designing conversational flows
• Integrating language services

Lab:

• Build a chatbot using Azure Bot Service
• Integrate Q&A and NLP capabilities
• Deploy chatbot to web interface

Outcome:
Create intelligent chatbots for customer interaction.

Module 7: Document Intelligence

Topics Covered:

• Form Recognizer / Document Intelligence
• Data extraction from forms and invoices
• Structured data processing

Lab:

• Extract data from invoices and forms
• Train a custom document model
• Automate document processing workflow

Outcome:
Automate data extraction from documents.

Module 8: AI Solution Deployment & Monitoring

Topics Covered:

• Deploy AI models to Azure
• Monitor performance and usage
• Secure AI solutions

Lab:

• Deploy an AI solution to Azure App Service
• Monitor using Azure tools
• Implement security and access control

Outcome:
Deploy, manage, and secure AI applications.

Module 9: Responsible AI Implementation

Topics Covered:

• Ethical AI principles
• Bias and fairness
• Transparency and accountability

Lab:

• Evaluate AI model outputs for bias
• Apply responsible AI guidelines
• Implement monitoring for ethical compliance

Outcome:
Build trustworthy and responsible AI systems.