AWS AI Cloud Engineer Course & Curriculum

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

Module 1: AI, ML & Cloud Foundations

5 hours covering AI fundamentals and AWS AI/ML ecosystem overview.

  • AI vs ML vs DL vs Generative AI
  • Enterprise AI use cases (NA market)
  • AWS AI/ML ecosystem overview
  • Responsible AI & governance
  • AI use-case mapping worksheet
  • AWS AI service selection guide

Module 2: AWS AI & ML Ecosystem Overview

5 hours on managed AI vs custom ML service selection.

  • AWS AI Services vs ML Services
  • Managed AI APIs vs model training
  • High-level architecture patterns
  • Cost models & quotas
  • IAM & service access basics
  • AWS AI reference architectures
  • Cost-aware service comparison

Module 3: Generative AI on AWS (Amazon Bedrock)

8 hours mastering Enterprise GenAI with Amazon Bedrock.

  • Amazon Bedrock architecture
  • Foundation Models (Claude, Titan, Llama)
  • Prompt engineering (enterprise patterns)
  • System prompts & guardrails
  • Model inference & cost control
  • RAG concepts on AWS
  • Security & data isolation
  • Bedrock model invocation & prompt tuning

Module 4: Natural Language AI Services

6 hours on NLP automation for enterprises.

  • Amazon Comprehend (sentiment, entities)
  • Key phrase extraction
  • Language detection
  • Custom classification
  • Text analytics pipelines
  • NLP API integrations
  • Text intelligence workflows

Module 5: Speech & Conversational AI

5 hours on Voice AI for contact centers and bots.

  • Amazon Transcribe
  • Amazon Polly
  • Speech-to-Text & Text-to-Speech
  • Real-time vs batch voice processing
  • Voice bot use cases
  • Voice transcription system development
  • Speech-enabled assistant

Module 6: Computer Vision & Document AI

6 hours on automation & document intelligence.

  • Amazon Rekognition (image/video analysis)
  • Object & face detection
  • Amazon Textract
  • Invoice, resume & ID extraction
  • Enterprise document workflows
  • OCR pipeline development
  • Document extraction APIs

Module 7: Enterprise Search & RAG on AWS

5 hours on knowledge bots & internal search.

  • Amazon OpenSearch basics
  • Vector search concepts
  • Embeddings strategy
  • RAG with Bedrock + OpenSearch
  • Indexing structured & unstructured data
  • Knowledge base chatbot development
  • PDF & document ingestion

Module 8: Custom ML with Amazon SageMaker

5 hours on ML lifecycle and deployment.

  • SageMaker Studio
  • Data prep & training
  • Built-in vs custom algorithms
  • Model deployment (endpoints)
  • ML pipelines & MLOps basics
  • Train & deploy ML model
  • ML inference API development

Module 9: Security, Governance & Deployment

5 hours on production-grade AWS AI systems.

  • IAM roles & policies
  • Secrets Manager & encryption
  • Monitoring with CloudWatch
  • Cost optimization
  • CI/CD for AI workloads
  • AI compliance considerations
  • Secure AWS AI architecture blueprint