Module 1: Cloud Fundamentals

1.1 Cloud Computing Models

1.2 Core Cloud Services

1.3 Identity & Access Management (IAM), Security & Compliance Basics

1.4 Billing, Cost Optimisation, 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

Module 2: AI Fundamentals and Python Fundamentals

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 Visualisation Techniques

2.7 Use Cases

2.8 Case Studies

Module 3: Data Engineering for AI

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 Cataloguing

3.5 Real-Time Data Streaming

3.6 Use Cases

3.7 Case Studies

Module 4: Cloud with AI

4.1 Managed AI/ML Platforms

4.2 Model Training, Deployment, and Inference on Cloud

4.3 Containerisation 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

Module 5: Generative AI and LLM Models

5.1 Transformer Architecture, Attention Mechanism, and Tokenisation

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

Module 6: Cloud with Generative AI and LLM Models

6.1 Deploying and Hosting LLMs on Cloud Platforms

6.2 Inference Optimisation 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

Module 7: AI Workloads on Cloud

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

Module 8: Retrieval-Augmented Generation (RAG)

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

Module 9: Fine-Tuning and Optimisation on Cloud

9.1 Full Fine-Tuning vs. Parameter-Efficient Fine-Tuning (PEFT)

9.2 Distributed Training and Hyperparameter Optimisation

9.3 Model Compression, Distillation, and Quantisation

9.4 Domain Adaptation and Continual Learning

9.5 Cloud Tools for Efficient Fine-Tuning

9.6 Use Cases

9.7 Case Studies

Module 10: Agentic AI on Cloud

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

Module 11: Evaluation, Monitoring, Security & Responsible AI

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

Module 12: Capstone Project

12.1 Problem Identification and Solution Planning

12.2 AI Model Development and Cloud Deployment

12.3 Deliverables

Optional Module: AI Agents for Cloud

1. What Are AI Agents?

2. Examples of AI Agents for Cloud Services

3. Significance of AI Agents in Cloud Services

4. Trends in AI Agents for Cloud Services

5. Importance of AI Agents

6. Types of AI Agents

7. Case Studies

8. Hands-On Activity