AI+ Cloud Practitioner™

Certificate Code: AT-110

Passing Score: 70% (35/50)

Exam Info: 50 MCQs, 90 Minutes

Tagline: Formerly known as AI+ Cloud™<br><br>Transform Cloud Computing with Cutting-Edge AI integration

Course Overview:

Prerequisites:

Tools Used:

• TensorFlow
• SHAP (SHapley Additive exPlanations)
• Amazon S3
• AWS SageMaker

Modules:

• 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

What You’ll Learn:

• AI Model Development — Students learn to construct, train, and optimize machine learning models utilizing cloud-based tools and services. This involves learning to choose methods, preprocess data, and optimize models.
• Mastering cloud AI model deployment — Learners will master cloud AI model deployment and integration into existing systems and workflows. Learn deployment pipelines, version control, and CI/CD procedures to seamlessly integrate AI solutions into production environments.
• Problem-Solving in AI and Cloud — You will learn to apply AI and cloud computing concepts to real-world problems, enhancing their problem-solving skills.
• Optimization Techniques — Emphasizing AI model development and cloud deployment, learners will learn to optimize AI models and processes for performance, scalability, and cost.

Career Opportunities:

• 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.

Exam Blueprint:

• Cloud Fundamentals - 5%
• AI Fundamentals and Python Fundamentals - 9%
• Data Engineering for AI - 9%
• Cloud with AI - 9%
• Generative AI and LLM Models - 9%
• Cloud with Generative AI and LLM Models - 9%
• AI Workloads on Cloud - 9%
• Retrieval-Augmented Generation (RAG) - 9%
• Fine-Tuning and Optimization on Cloud - 8%
• Agentic AI on Cloud - 8%
• Evaluation, Monitoring, Security & Responsible AI - 8%
• Capstone Project - 8%

Self-Study Materials:

• Videos: Engaging visual content to enhance understanding and learning experience.
• Podcasts: Insightful audio sessions featuring expert discussions and real-world cases.
• Audiobooks: Listen and learn anytime with convenient audio-based knowledge sharing.
• E-Books: Comprehensive digital guides offering in-depth knowledge and learning support.
• Labs: Interactive lab sessions to apply concepts and strengthen technical skills.
• Module Wise Quizzes: Interactive assessments to reinforce learning and test conceptual clarity.
• Additional Resources: Supplementary references and list of tools to deepen knowledge and practical application.

Frequently Asked Questions:

• Q: How is the course structured?
  A: 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.
• Q: Who should enroll in this certification?
  A: 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.
• Q: What practical skills will I gain from this course?
  A: 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.
• Q: How does this certification benefit my career?
  A: 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.
• Q: What kind of projects will I work on during the course?
  A: 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.

Certificate Features:

• High-Quality Video, E-book & Audiobook
• Modules Quizzes
• AI Mentor
• Access for Tablet & Phone
• Online Proctored Exam with One Free Retake
• LABs Practices