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AI+ Cloud Practitioner™

AI+ Cloud Practitioner™
  • 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
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Certificate Code

AT-110

Exam Format

AI-Driven Remote Exam Proctoring

Course Overview

Important details and certification information

Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
Key concepts in both AI, Fundamental understanding of computer science, Familiarity with cloud computing platforms like AWS, Azure, or GCP
50 questions, 70% passing, 90 minutes, online proctored exam

Certification Modules

  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

  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

  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

  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

  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

  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

  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

  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

  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

  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

  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

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

  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

AI Tools Covered

TensorFlow
SHAP (SHapley Additive exPlanations)
Amazon S3
AWS SageMaker
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