Courses AI certifications (AI CERTs®)

AI+ Cloud Practitioner™

  • Price 279 EUR + VAT
  • E-learning Start any time
  • Duration 40 hours of content, self-paced
  • Language English
  • Topic AI Cloud

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Overview

What the course covers

The course, the materials and the exam are in English. The description below is the original AI CERTs® text.

  • 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

Prerequisites

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

Audience

Who is it for

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.

In the price

What the price includes

  • Self-paced online course with the full learning material
  • The official AI CERTs® exam
  • Digital badge after passing the exam

Course content

Syllabus

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

Exam

About the exam

  • 50 Questions
  • 90 min Time limit
  • 70% Pass mark
  • Online proctored exam

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