AI+ Cloud Practitioner™
AT-110
Formerly known as AI+ Cloud™<br><br>Transform Cloud Computing with Cutting-Edge AI integration- 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
Why This Certification Matters
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
At a Glance: Course + Exam Overview
Program Name
AI+ Cloud Practitioner™
Included
Instructor-led OR Self-paced course + Official exam + Digital badge
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
Exam Format
50 questions, 70% passing, 90 minutes, online proctored exam
Delivery
Online labs, projects, case studies
Outcome
Industry-recognized credential + hands-on experience
Job Roles & Industry Outlook
Cloud Architect / Engineer
Data Analyst / Engineer
Robotics Engineer
Cloud Professionals
System Architect
AI/ML Engineer
Industry Growth: AI+ Cloud Practitioner™
- 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.
What You'll Learn
- 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
- 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
Tools You'll Explore
TensorFlow
SHAP (SHapley Additive exPlanations)
Amazon S3
AWS SageMaker
Prerequisites
- Key concepts in both AI, Fundamental understanding of computer science, Familiarity with cloud computing platforms like AWS, Azure, or GCP
Exam Details
Duration
90 minutes
Passing Score
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via AI proctored exam platform (flexible scheduling)
Exam Blueprint
- Module 1: Cloud Fundamentals - 7%
- Module 2: AI Fundamentals and Python Fundamentals - 7%
- Module 3: Data Engineering for AI - 7%
- Module 4: Cloud with AI - 7%
- Module 5: Generative AI and LLM Models - 8%
- Module 6: Cloud with Generative AI and LLM Models - 8%
- Module 7: AI Workloads on Cloud - 8%
- Module 8: Retrieval-Augmented Generation (RAG) - 8%
- Module 9: Fine-Tuning and Optimization on Cloud - 8%
- Module 10: Agentic AI on Cloud - 8%
- Module 11: Evaluation, Monitoring, Security & Responsible AI - 8%
- Module 12: Capstone Project - 8%
- Optional Module: AI Agents for Cloud - 8%
Choose the Format That Fits Your Schedule
What's Included (One-Year Subscription + All Updates):
- High-Quality Videos, E-book (PDF & Audio), and Podcasts
- AI Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Online Proctored Exam with One Free Retake
- Comprehensive Exam Study Guide
- Access for Tablet & Phone
Frequently Asked Questions
The course includes a mix of theoretical knowledge and practical applications, culminating in an interactive capstone project. This structure ensures that participants gain both conceptual understanding and hands-on experience.
This course is ideal for developers, IT professionals, and anyone with a foundational understanding of AI and cloud computing who wants to enhance their skills in integrating AI with cloud platforms like AWS, Azure, or Google Cloud.
Participants will learn to develop, deploy, and manage AI models on leading cloud platforms. Skills include optimizing AI model performance, ensuring security, meeting compliance standards, and applying AI and cloud concepts to solve real-world problems.
This certification enhances your professional profile by demonstrating proficiency in integrating AI with cloud computing. It equips you with in-demand skills, giving you a competitive edge in the job market and opening doors to lucrative career opportunities.
The certification includes an interactive capstone project where participants apply their knowledge to design and implement AI solutions within cloud environments. This project is designed to simulate real-world scenarios and challenges.