Ai-102: Become An Azure Ai Engineer In One Weekend [2025]

Learn how to use Azure Cognitive Services to pass the AI-102 and utilize AI in your own applications

What you'll learn

Design and manage Azure AI solutions end-to-end aligned to the AI-102 exam.

Build NLP, vision, and document intelligence with Azure Cognitive Services.

Implement generative AI with Azure OpenAI and Retrieval-Augmented Generation.

Integrate CLU and Bot Framework to deliver secure, conversational AI on Azure.

Operationalize AI: monitor, cost-optimize, and scale workloads using Azure tools.

Apply Responsible AI, data privacy, and security to meet enterprise compliance.

Requirements

A free or paid subscription to Microsoft Azure

Excitement to learn Microsoft's constantly growing cloud platform

Ideally, previous experience with Visual Studio

Previous coding experience in C# or similar language

Description

Covers the requirements of Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution. Updated for the April 30, 2025 skills outline so you’re learning exactly what’s measured today.Continuously improved since launch with new quizzes, hands-on labs, and downloadable resources—so you can track progress and practice with realistic scenarios that mirror the exam’s style and scope.Built for the Azure AI Engineer role. As an Azure AI engineer, you’ll design, build, and ship solutions using Azure AI services across the full lifecycle—requirements, development, deployment, integration, performance tuning, and monitoring—often in partnership with architects, data scientists, and engineers.Prerequisites & target audience. You should be comfortable developing in C# or Python and able to work with REST APIs and SDKs to build secure solutions in computer vision, video, natural language processing, knowledge mining, and generative AI—with a working knowledge of Azure AI components, data storage options, and Responsible AI practices. What the exam measures (latest blueprint)Plan and manage an Azure AI solution (20–25%)Implement generative AI solutions (15–20%)Implement an agentic solution (5–10%)Implement computer vision solutions (10–15%)Implement natural language processing solutions (15–20%)Implement knowledge mining and information extraction solutions (15–20%) Why this courseAligned to the official skills outline and refreshed when Microsoft updates the exam.Hands-on first: build real Azure AI solutions—vision, language, search, bots, and gen-AI—so you’re ready for both the exam and on-the-job work.Exam-style practice: scenario questions and labs that reinforce the exact capabilities Microsoft evaluates.Who this course is forEngineers and developers working with Azure who want a structured path to the Azure AI Engineer Associate credential.Teams building AI features on Azure and seeking shared patterns for secure, reliable deployments.Professionals aiming to pass Exam AI-102 and validate practical skills with Azure AI services.

Overview

Section 1: Introduction

Lecture 1 Course Introduction

Section 2: Foundations of Azure AI: Principles, Services, and First Steps

Lecture 2 Evolution of AI & Azure’s Role

Lecture 3 Core AI Sub‑domains

Lecture 4 Responsible AI Principles

Lecture 5 Azure Cognitive Services Portfolio

Lecture 6 Setting Up Your First Azure AI Resource

Section 3: Applied Language AI on Azure: Moderation, Analytics & Understanding

Lecture 7 Content Moderator for Text

Lecture 8 Text Analytics Fundamentals

Lecture 9 Designing a Feedback Sorter (Queues + Functions)

Lecture 10 Language Understanding (LUIS) Concepts

Lecture 11 Integrating LUIS into Bots & Apps

Section 4: Voice AI on Azure: Recognition, Translation & Natural Synthesis

Lecture 0 Speech‑to‑Text Essentials

Lecture 12 Speech Translation Workflow

Lecture 13 Synthesizing Natural Speech (Text‑to‑Speech)

Lecture 14 Entity Recognition with LUIS + Speech

Lecture 15 Real‑time Transcription Solutions

Section 5: Vision AI on Azure: Image, Face & Video Insights

Lecture 16 Computer Vision API Overview

Lecture 17 Detecting Faces & Emotions (Face API)

Lecture 18 Building Custom Image Classifiers

Lecture 19 Extracting Video Insights (Video Indexer)

Lecture 20 Implementing Prediction Endpoints

Section 6: Advanced Language AI on Azure: CLU Schema, Summarization & Secure Ops

Lecture 21 Deep Dive into LUIS Schema

Lecture 22 Conversational Intelligence Patterns

Lecture 23 Text Summarization & Entity Linking

Lecture 24 Key Phrase & Sentiment at Scale

Lecture 25 Securing & Deploying Language Resources

Section 7: Operational AI Governance on Azure: Compliance, Monitoring & Security

Lecture 26 Regulatory Landscapes & Azure Controls

Lecture 27 Data Minimization & Retention

Lecture 28 Monitoring AI Workloads

Lecture 29 Content Moderation via .NET SDK

Lecture 30 Securing Cognitive Service Endpoints

Section 8: Bot Development on Azure: Architecture, Channels, DevOps & Security

Lecture 31 Bot Service Architecture & SDK

Lecture 32 Creating Bots from Templates

Lecture 33 Integrating AI Services into Bots

Lecture 34 Multi‑channel Deployment

Lecture 35 Monitoring, Telemetry & DevOps

Lecture 36 Securing & Governing Bots

Section 9: Course Closer

Lecture 37 Course Closure & Next Steps

Developers preparing for Microsoft Azure AI-102 (Azure AI Engineer Associate).,C# or Python engineers building NLP, vision, and generative AI on Azure.,Solution architects and tech leBot developers integrating CLU, Azure OpenAI, and Bot Framework into apps.