1.1 Cloud Computing Models
1.2 Core Cloud Services
1.3 Identity & Access Management (IAM), Security & Compliance Basics
1.4 Billing, Cost Optimization, and Cloud Economics
1.5 Multi-cloud Concepts
1.6 Infrastructure as Code (IaC) Basics with Terraform
1.7 Use Cases
1.8 Case Studies
1.9 Hands-On Activity

2.1 Introduction to Artificial Intelligence, Machine Learning Types
2.2 Neural Networks and Deep Learning Fundamentals
2.3 Python Programming
2.4 Essential Libraries
2.5 Mathematics for AI
2.6 Data Preprocessing, Exploration, and Visualization Techniques
2.7 Use Cases
2.8 Case Studies

3.1 Data Collection, Storage, and Processing Pipelines (ETL/ELT)
3.2 Big Data Technologies
3.3 Data Lakes, Data Warehouses, and Feature Stores
3.4 Data Quality, Governance, Versioning, and Cataloging
3.5 Real-Time Data Streaming
3.6 Use Cases
3.7 Case Studies

4.1 Managed AI/ML Platforms
4.2 Model Training, Deployment, and Inference on Cloud
4.3 Containerization with Docker and Orchestration with Kubernetes
4.4 Serverless AI Architectures
4.5 Scaling and Monitoring AI Workloads
4.6 Use Cases
4.7 Case Studies

5.1 Transformer Architecture, Attention Mechanism, and Tokenization
5.2 Major LLM Families: GPT, Llama, Gemini, Claude, Mistral
5.3 Prompt Engineering Techniques
5.4 Generative Model Lifecycle
5.5 Multimodal Generative AI
5.6 Use Cases
5.7 Case Studies

6.1 Deploying and Hosting LLMs on Cloud Platforms
6.2 Inference Optimization Techniques
6.3 Integration with Cloud-Native Services
6.4 Cost Governance for GenAI Workloads
6.5 Hybrid and Edge Deployment Strategies
6.6 Use Cases
6.7 Case Studies

7.1 MLOps Lifecycle and Best Practices
7.2 Experiment Tracking (MLflow), Model Versioning, and CI/CD Pipelines
7.3 Model Monitoring and Performance Drift Detection
7.4 Orchestration Tools: SageMaker Pipelines, Vertex AI Pipelines, Kubeflow
7.5 Use Cases
7.6 Case Studies

8.1 RAG Architecture and Components
8.2 Vector Databases and Embeddings
8.3 Advanced RAG Patterns
8.4 Evaluation Metrics for RAG Systems
8.5 Cloud-Native Vector Search Services
8.6 Use Cases
8.7 Case Studies

9.1 Full Fine-Tuning vs. Parameter-Efficient Fine-Tuning (PEFT)
9.2 Distributed Training and Hyperparameter Optimization
9.3 Model Compression, Distillation, and Quantization
9.4 Domain Adaptation and Continual Learning
9.5 Cloud Tools for Efficient Fine-Tuning
9.6 Use Cases
9.7 Case Studies

10.1 AI Agents Fundamentals
10.2 Distributed Frameworks: LangGraph, CrewAI, AutoGen, Semantic Kernel
10.3 Multi-Agent Systems and Orchestration
10.4 Autonomous Workflows and Decision Engines
10.5 Cloud Deployment of Agentic Systems
10.6 Use Cases
10.7 Case Studies

11.1 Comprehensive LLM and GenAI Evaluation Frameworks
11.2 Bias Detection, Fairness, and Explainability
11.3 Security Threats
11.4 Guardrails, Content Moderation, and Compliance (GDPR, SOC2)
11.5 Responsible AI Governance and Audit Practices
11.6 Use Cases
11.7 Case Studies

12.1 Problem Identification and Solution Planning
12.2 AI Model Development and Cloud Deployment
12.3 Deliverables