Description

Introduction of Azure AI Engineer Associate

The AI-102: Designing and Implementing an Azure AI Solution certification validates expertise in designing, managing, and deploying AI solutions using Microsoft Azure AI services. This Azure AI Engineer Associate training provides hands-on knowledge of Azure AI capabilities, including Azure Cognitive Services, Azure Machine Learning, and AI-based application integration.

Prerequisites

• Basic understanding of AI and Machine Learning concepts
• Familiarity with Azure fundamentals
• Experience with Python or C# programming (recommended but not mandatory)
• Knowledge of REST APIs and JSON data format

Table of Contents

1. Introduction to Azure AI and AI-102 Certification

1.1 Overview of AI-102 Certification and Exam Objectives
1.2 Role of an Azure AI Engineer
1.3 Understanding Azure AI Ecosystem
1.4 Setting Up an Azure AI Environment

2. Implementing Computer Vision Solutions

2.1 Introduction to Azure Computer Vision Service
2.2 Analyzing Images with Pre-trained AI Models
2.3 Implementing Custom Vision Models
2.4 Hands-On: Object Detection and Image Classification

3. Implementing Natural Language Processing (NLP) Solutions

3.1 Introduction to Azure Text Analytics and Sentiment Analysis
3.2 Building a Chatbot with Azure Bot Service(Ref: Microsoft Certified: Azure Data Scientist Associate (DP-100))
3.3 Speech-to-Text and Text-to-Speech Integration
3.4 Hands-On: Creating an AI-Powered Virtual Assistant

4. Developing Conversational AI Solutions

4.1 Introduction to Azure Bot Framework
4.2 Integrating Bots with Microsoft Teams and Web Applications
4.3 Implementing Language Understanding (LUIS)
4.4 Hands-On: Deploying a Custom AI Chatbot

5. Implementing Knowledge Mining Solutions

5.1 Overview of Azure Cognitive Search
5.2 Implementing AI-Powered Document Search
5.3 Leveraging AI for Data Indexing and Summarization
5.4 Hands-On: Enhancing Search Capabilities with AI

6. Implementing Speech AI Solutions

6.1 Introduction to Azure Speech Services
6.2 Building Real-Time Speech Translation Applications
6.3 Enhancing AI with Speaker Recognition and Speech Synthesis
6.4 Hands-On: Implementing Speech Recognition in Applications

7. Implementing Responsible AI and AI Ethics

7.1 Understanding Responsible AI Principles
7.2 Implementing Bias Detection and AI Governance
7.3 Monitoring and Improving AI Model Performance
7.4 Hands-On: Applying AI Fairness and Explainability

8. Deploying AI Models with Azure Machine Learning

8.1 Introduction to Azure Machine Learning Service
8.2 Training and Deploying Custom ML Models
8.3 Automating AI Model Management with MLOps
8.4 Hands-On: Deploying an AI Model to Azure AI Services

9. Securing and Monitoring AI Solutions

9.1 Managing Authentication and Authorization for AI Services
9.2 Implementing AI Monitoring and Logging Best Practices
9.3 Performance Optimization for AI Workloads
9.4 Hands-On: Securing and Scaling an AI-Based Application

10. Preparing for AI-102 Certification Exam

10.1 Exam Format, Question Types, and Study Tips
10.2 Best Practices for Time Management and Hands-on Labs
10.3 Sample Practice Questions and Case Studies
10.4 Final Hands-On: Simulated AI-102 Exam Project

Conclusion

This Azure AI Engineer Associate course equips participants with practical AI engineering skills using Microsoft Azure AI Services. Upon completion, participants will be ready to pass the AI-102 certification exam, enabling them to design, develop, and implement AI-driven solutions in real-world applications.

Reference