AI+ Cloud Practitioner™

Learning Modules

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 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

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 Visualization 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 Cataloging
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 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

Module 5: Generative AI and LLM Models

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

Module 6: Cloud with Generative AI and LLM Models

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

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 Optimization on Cloud

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

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

AI Enabled Coordinated Assurance Certification

Certificate Code:

AT-110

Duration:

Instructor-Led: 5 days (live or virtual)
Self-Paced: 40 hours of content

Prerequisites:

Key concepts in both AI, Fundamental understanding of computer science, Familiarity with cloud computing platforms like AWS, Azure, or GCP

Why This Certification Matters

Leverage AI for Smarter Leadership Decisions:

Learn how to harness AI tools to streamline operations, enhance strategic planning, and drive performance.

Enhance AI Integration Across the Organization:

Use AI to accelerate the integration of AI-driven solutions, automating processe.

Stay Ahead in AI-Driven Innovation:

As demand for AI expertise rises, Chief AI Officers with advanced AI knowledge are highly sought after to spearhead AI.

Boost Strategic Decision-Making with AI Analytics:

Master AI models to analyze business data, predict outcomes, and enable more informed, real-time decisions.

Advance Your Career in AI Leadership:

With AI reshaping industries, this certification equips you with the skills needed to lead AI initiatives.

Who Should Enroll

Cloud Professionals: Enhance your cloud management skills by integrating AI to optimize cloud performance, improve resource utilization.
Cloud Architects & Engineers: Learn to leverage AI to design scalable cloud infrastructures, automate cloud provisioning, and enhance security.
IT Infrastructure Managers: Use AI to optimize cloud deployment, automate system management, and improve cloud security and disaster recovery planning.
Business Leaders: Drive innovation in your organization by adopting AI in cloud technologies to enhance scalability, reduce costs, and optimize cloud solutions.
Students & Fresh Graduates: Gain a competitive edge in the cloud computing field by mastering AI tools and techniques that are revolutionizing cloud infrastructure.