Azure AI Engineer Associate: Designing and Implementing Microsoft AI Solutions (AI-102)
Unlock the power of Artificial Intelligence through the Azure Cloud! This course will transform you into an expert in Azure Cognitive Services, Machine Learning, and Knowledge Mining. If you are a software developer or data engineer looking to build AI-powered applications, the AI-102 certification is the ultimate milestone for your career. Start your AI journey with Namifly today!
4.8
By Namifly
Last updated: March 2026
$1000
One-time payment
30-day money-back guarantee
This course includes:
Preparing to Develop AI Solutions on Azure (Environment Setup & Azure Portal)
Creating and Managing Cognitive Services (Security, Pricing, & Monitoring)
Developing Natural Language Processing Solutions (Text Analytics & CLU)
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Course Description
Artificial Intelligence (AI) is no longer a glimpse into the future—it is the engine driving the modern world.
The Microsoft Azure AI-102 exam is the industry gold standard for validating your ability to build, manage, and deploy intelligent solutions using Azure’s cutting-edge AI services. At Namifly, we have meticulously crafted this training program to bridge the gap between theoretical cloud concepts and real-world industrial application.
Why Choose This Course?
In today’s digital landscape, the demand for Natural Language Processing (NLP), Computer Vision, and Conversational AI is skyrocketing. Companies are no longer looking for just "developers"; they are searching for AI Engineers who can integrate vision APIs to extract data from images, build sophisticated bots for customer engagement, and implement speech-to-text services for global accessibility.
What You Will Master:
Cognitive Services Integration: Learn to seamlessly integrate Vision, Language, and Speech APIs into your existing applications.
Natural Language Processing (NLP): Deep dive into sentiment analysis, entity recognition, and language understanding to make machines "read" and "understand."
Knowledge Mining: Master Azure Cognitive Search to extract actionable insights from vast amounts of unstructured data.
Conversational AI: Build and deploy intelligent, human-like chatbots using the Azure Bot Service and Framework.
Security & Scalability: Learn how to secure your AI endpoints and monitor performance to ensure your solutions are production-ready.
The Namifly Advantage
We don't just teach theory. Our curriculum is built around Hands-on Labs and Real-World Scenarios. You will work on projects that mimic actual industry challenges, ensuring that you don't just pass the Microsoft official exam on your first attempt, but you also gain the confidence to lead AI projects in a corporate environment. Investing in this course is investing in a future-proof career in the 2026 AI-driven economy.
Course Curriculum
Expand the sections below to see the detailed curriculum for this course.
Introduction to AI on Azure: Understanding the Azure AI ecosystem.
Environment Setup: Configuring Visual Studio Code, Azure CLI, and SDKs.
Azure Resource Management: Creating and managing AI resources in the Azure Portal.
AI Strategy: Designing a roadmap for AI solution development.
Resource Provisioning: Deploying multi-service and single-service resources.
Security & Authentication: Implementing API keys, Azure Key Vault, and Managed Identities.
Monitoring & Logging: Using Azure Monitor and Application Insights to track AI performance.
Containerization: Deploying Cognitive Services in Docker containers for edge computin
Image Analysis: Extracting visual features, tags, and descriptions using the Computer Vision API.
Custom Vision: Training and deploying custom image classification and object detection models.
Video Analytics: Processing real-time video streams with Azure Video Indexer.
Face API: Implementing facial recognition and attribute detection.
Text Analytics: Sentiment analysis, key phrase extraction, and entity recognition.
Conversational Language Understanding (CLU): Building intent-based models for apps.
Azure AI Translator: Implementing real-time text and document translation.
Speech Services: Integrating Speech-to-Text and Text-to-Speech capabilities.
Azure AI Search: Creating and configuring an intelligent search index.
Skillset Development: Building custom enrichment pipelines with AI skills.
Indexing Unstructured Data: Extracting insights from PDFs, images, and complex documents.
Knowledge Store: Persisting enriched data for further analysis and visualization.
Azure Bot Framework: Creating sophisticated bots with Bot Framework Composer.
Integration with CLU: Powering chatbots with natural language understanding.
Multi-Channel Deployment: Deploying bots to Web Chat, Microsoft Teams, and Telegram.
Extended Bot Logic: Implementing complex dialogs, triggers, and state management.
AI-102 Exam Prep: Strategy for tackling case studies and technical questions.
Full-Length Mock Tests: Simulating the real Microsoft certification environment.
Hands-on Project Review: Finalizing your portfolio project for recruitment.
Interview Coaching: Common AI Engineer interview questions and answers.
Instructor information not available.
4.8
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Frequently Asked Questions
Find answers to common questions about this course.
Yes. A basic understanding of C# or Python is necessary, as we will demonstrate implementation using Azure SDKs.
With the right preparation and hands-on lab practice, it is very manageable. Our course includes discussions on all sample exam questions to ensure your success.
