• Cloud-AI Fusion: Learn to integrate AI into scalable cloud environments

• Advanced Infrastructure: Master CI/CD, cloud AI models, and deployment strategies

• Capstone Project: Gain hands-on experience with real-world applications

• Future-Ready Skills: Prepares professionals to lead AI-powered cloud innovation

Module 1: Cloud Fundamentals

1. 1.1 Cloud Computing Models
2. 1.2 Core Cloud Services
3. 1.3 Identity & Access Management (IAM), Security & Compliance Basics
4. 1.4 Billing, Cost Optimization, and Cloud Economics
5. 1.5 Multi-cloud Concepts
6. 1.6 Infrastructure as Code (IaC) Basics with Terraform
7. 1.7 Use Cases
8. 1.8 Case Studies
9. 1.9 Hands-On Activity

Module 2: AI Fundamentals and Python Fundamentals

1. 2.1 Introduction to Artificial Intelligence, Machine Learning Types
2. 2.2 Neural Networks and Deep Learning Fundamentals
3. 2.3 Python Programming
4. 2.4 Essential Libraries
5. 2.5 Mathematics for AI
6. 2.6 Data Preprocessing, Exploration, and Visualization Techniques
7. 2.7 Use Cases
8. 2.8 Case Studies

Module 3: Data Engineering for AI

1. 3.1 Data Collection, Storage, and Processing Pipelines (ETL/ELT)
2. 3.2 Big Data Technologies
3. 3.3 Data Lakes, Data Warehouses, and Feature Stores
4. 3.4 Data Quality, Governance, Versioning, and Cataloging
5. 3.5 Real-Time Data Streaming
6. 3.6 Use Cases
7. 3.7 Case Studies

Module 4: Cloud with AI

1. 4.1 Managed AI/ML Platforms
2. 4.2 Model Training, Deployment, and Inference on Cloud
3. 4.3 Containerization with Docker and Orchestration with Kubernetes
4. 4.4 Serverless AI Architectures
5. 4.5 Scaling and Monitoring AI Workloads
6. 4.6 Use Cases
7. 4.7 Case Studies

Module 5: Generative AI and LLM Models

1. 5.1 Transformer Architecture, Attention Mechanism, and Tokenization
2. 5.2 Major LLM Families: GPT, Llama, Gemini, Claude, Mistral
3. 5.3 Prompt Engineering Techniques
4. 5.4 Generative Model Lifecycle
5. 5.5 Multimodal Generative AI
6. 5.6 Use Cases
7. 5.7 Case Studies

Module 6: Cloud with Generative AI and LLM Models

1. 6.1 Deploying and Hosting LLMs on Cloud Platforms
2. 6.2 Inference Optimization Techniques
3. 6.3 Integration with Cloud-Native Services
4. 6.4 Cost Governance for GenAI Workloads
5. 6.5 Hybrid and Edge Deployment Strategies
6. 6.6 Use Cases
7. 6.7 Case Studies

Module 7: AI Workloads on Cloud

1. 7.1 MLOps Lifecycle and Best Practices
2. 7.2 Experiment Tracking (MLflow), Model Versioning, and CI/CD Pipelines
3. 7.3 Model Monitoring and Performance Drift Detection
4. 7.4 Orchestration Tools: SageMaker Pipelines, Vertex AI Pipelines, Kubeflow
5. 7.5 Use Cases
6. 7.6 Case Studies

Module 8: Retrieval-Augmented Generation (RAG)

1. 8.1 RAG Architecture and Components
2. 8.2 Vector Databases and Embeddings
3. 8.3 Advanced RAG Patterns
4. 8.4 Evaluation Metrics for RAG Systems
5. 8.5 Cloud-Native Vector Search Services
6. 8.6 Use Cases
7. 8.7 Case Studies

Module 9: Fine-Tuning and Optimization on Cloud

1. 9.1 Full Fine-Tuning vs. Parameter-Efficient Fine-Tuning (PEFT)
2. 9.2 Distributed Training and Hyperparameter Optimization
3. 9.3 Model Compression, Distillation, and Quantization
4. 9.4 Domain Adaptation and Continual Learning
5. 9.5 Cloud Tools for Efficient Fine-Tuning
6. 9.6 Use Cases
7. 9.7 Case Studies

Module 10: Agentic AI on Cloud

1. 10.1 AI Agents Fundamentals
2. 10.2 Distributed Frameworks: LangGraph, CrewAI, AutoGen, Semantic Kernel
3. 10.3 Multi-Agent Systems and Orchestration
4. 10.4 Autonomous Workflows and Decision Engines
5. 10.5 Cloud Deployment of Agentic Systems
6. 10.6 Use Cases
7. 10.7 Case Studies

Module 11: Evaluation, Monitoring, Security & Responsible AI

1. 11.1 Comprehensive LLM and GenAI Evaluation Frameworks
2. 11.2 Bias Detection, Fairness, and Explainability
3. 11.3 Security Threats
4. 11.4 Guardrails, Content Moderation, and Compliance (GDPR, SOC2)
5. 11.5 Responsible AI Governance and Audit Practices
6. 11.6 Use Cases
7. 11.7 Case Studies

Module 12: Capstone Project

1. 12.1 Problem Identification and Solution Planning
2. 12.2 AI Model Development and Cloud Deployment
3. 12.3 Deliverables

Optional Module: AI Agents for Cloud

1. 1. What Are AI Agents?
2. 2. Examples of AI Agents for Cloud Services
3. 3. Significance of AI Agents in Cloud Services
4. 4. Trends in AI Agents for Cloud Services
5. 5. Importance of AI Agents
6. 6. Types of AI Agents
7. 7. Case Studies
8. 8. Hands-On Activity

Cloud AI Integration Specialist

Focuses on integrating AI tools into cloud systems, optimizing cloud performance, scalability, and security.

AI Cloud Architect

Designs AI-powered cloud infrastructure, creating scalable, efficient, and secure cloud environments for organizations.

Cloud Automation Expert

Implements AI-driven automation tools for managing cloud infrastructure, reducing manual intervention and improving operational efficiency.

AI Cloud Data Scientist

Uses AI algorithms and data analytics to analyze cloud-based data, providing insights for better decision-making and resource management.

Cloud Security AI Specialist

AI technologies are applied to enhance cloud security, detecting anomalies, predicting threats, and ensuring robust protection of cloud.