Opleiding: AI+ Cloud Practitioner™ eLearning

Formerly known as AI+ Cloud™

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

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.

• 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.

• 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

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

Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.

De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.

Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.