Azure AI Cloud Engineer Course & Curriculum

NCPL's hands-on Azure AI Cloud Engineer training — full curriculum, real projects, certification prep, and job placement support. We train, mentor and place you.

Module 1: AI & Cloud Foundations

5 hours covering AI fundamentals and Azure AI ecosystem overview.

  • AI vs ML vs DL vs Generative AI (industry clarity)
  • Real-world AI use cases in global markets
  • Azure AI ecosystem overview
  • Responsible AI (Microsoft framework)
  • AI use-case mapping (job-oriented)
  • Azure AI service selection framework

Module 2: Azure AI Ecosystem Overview

5 hours on Azure AI service selection for real projects.

  • Azure AI Services vs Azure Machine Learning
  • Azure AI Studio & AI Foundry
  • Azure OpenAI Service architecture
  • API-based AI vs model-training AI
  • Cost & quota management (enterprise)
  • Azure AI architecture diagrams
  • Cost-optimized AI design patterns

Module 3: Azure OpenAI Service

8 hours mastering Generative AI with Azure OpenAI.

  • Azure OpenAI deployment models
  • GPT chat, embeddings, completions
  • Enterprise prompt engineering
  • System / User / Assistant roles
  • Token usage & cost control
  • Content safety & moderation
  • RAG (Retrieval Augmented Generation)
  • Deploy GPT models & build chat APIs

Module 4: Azure AI Language Services

6 hours on NLP for business automation.

  • Sentiment analysis
  • Named Entity Recognition
  • Key phrase extraction
  • Language detection
  • Question Answering systems
  • NLP microservices development
  • API integrations

Module 5: Azure AI Speech Services

5 hours on Voice AI development.

  • Speech-to-Text
  • Text-to-Speech
  • Speech translation
  • Real-time vs batch processing
  • Call-center & voice-bot use cases
  • Voice assistant demo development
  • Speech transcription pipeline

Module 6: Azure AI Vision & Document Intelligence

6 hours on automation & document processing.

  • Image analysis & OCR
  • Object detection
  • Azure Document Intelligence
  • Resume, invoice, ID extraction
  • Business automation use cases
  • OCR pipelines development
  • Document extraction workflows

Module 7: Azure AI Search & RAG

5 hours on enterprise knowledge systems.

  • Azure AI Search fundamentals
  • Indexing structured & unstructured data
  • Semantic & vector search
  • RAG architecture with Azure OpenAI
  • Build RAG chatbot using PDFs & docs

Module 8: Azure Machine Learning

5 hours on custom ML and deployment.

  • Azure ML workspace
  • Data prep & training
  • Model registry
  • Endpoint deployment
  • MLOps fundamentals
  • Train & deploy ML model
  • ML inference API development

Module 9: Security, Governance & Deployment

5 hours on production readiness.

  • Managed identities
  • Azure Key Vault
  • RBAC (Role-Based Access Control)
  • Monitoring & logging
  • CI/CD for AI workloads
  • Cost governance
  • Secure AI reference architecture