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AI+ Audio Practitioner™ eLearning
Formerly known as AI+ Audio™
Experience the power of AI in Audio to reinvent music production, elevate sound design, and craft immersive auditory experiences.
Empower Audio Innovation with AI: Creative, Practical, Transformative
Beginner-Friendly Learning: Perfect for newcomers eager to explore AI-powered audio, covering essential concepts with ease
Comprehensive Skill Building: Includes speech processing, sound enhancement, voice synthesis, and real-world audio AI applications
Industry-Ready Expertise: Understand how AI is reshaping music, media, entertainment, and communication sectors
Hands-On Direction: Provides practical frameworks and guided exercises to help you create, analyse, and optimise audio using AI
Aspiring Audio Engineers – Ideal for those looking to integrate AI into sound design, mixing, and mastering.
Music Producers and Composers – Perfect for creators who want to use AI tools for music generation and adaptive composition.
Machine Learning Enthusiasts – Great for learners eager to apply ML models to audio analysis and synthesis.
Game and Media Developers – Suitable for professionals aiming to create intelligent, immersive, and responsive sound environments.
Tech Innovators and Researchers – Designed for individuals exploring cutting-edge AI applications in audio technology and digital sound innovation.
Revolutionizes Sound CreationLearn how AI automates composition, mixing, and mastering, making audio production faster and more innovative.
Enhances Audio QualityUse AI tools to clean, balance, and optimize sound for professional-grade results across platforms.
Personalizes Listening ExperiencesDiscover how AI tailors music and soundscapes to individual preferences in real time.
Bridges Creativity and TechnologyCombine artistic vision with AI-driven tools to create immersive, next-generation audio experiences.
Expands Career OpportunitiesGain industry-ready skills for roles in music tech, sound design, gaming, and multimedia production.
Module 1: Introduction to AI and Sound
1.1 What is AI?
1.2 AI in Daily Life: Audio Examples
1.3 Basics of Sound Waves, Amplitude, Frequency
1.4 Digital Audio Fundamentals
Module 2: Harnessing AI Across Audio Domains
2.1 AI for Audio Enhancement and Restoration
2.2 AI for Audio Accessibility and Personalization
2.3 AI in Speech and Voice Technologies
2.4 Popular Audio Libraries: Librosa, PyAudio
2.5 Use Case:AI-Driven Real-Time Captioning and Translation for Live Events
2.6 Case Study:Personalized Hearing Aid Adaptation Using AI and Smart Earbuds
2.7 Hands-on: Voice Emotion Detection using Deepgram’s Voice AI Platform
Module 3: Machine Learning & AI for Audio
3.1 Machine Learning Models for Audio Applications
3.2 Deep Learning & Advanced AI Techniques for Audio
3.3 Audio-Specific Architectures: CNNs, RNNs, Transformers
3.4 Transfer Learning in Audio AI
3.5 Use Case: Speech-to-Text Transcription for Medical Records
3.6 Case Study: AI-powered Music Generation with Deep Learning
3.7 Hands-on: Build a Speech-to-Text Model Using TensorFlow
Module 4: Speech Recognition & Text-to-Speech
4.1 Fundamentals of Speech Recognition & Phonetics
4.2 API-based ASR Solutions
4.3 Building Custom ASR Models with Transformers
4.4 Introduction to TTS & Voice Cloning
4.5 Use Case: Automating Meeting Transcriptions with Google Speech-to-Text API
4.6 Case Study: Custom Transformer-based ASR Model for Multilingual Customer Support
4.7 Hands-on: Transcribe audio with an ASR API; generate speech from text
Module 5: Audio Enhancement & Noise Reduction
5.1 Common Audio Issues
5.2 AI-based Noise Filtering & Enhancement
5.3 Use Cases: Enhancing Audio Quality for Remote Work Calls Using AI Noise Reduction
5.4 Case Study: Krisp’s AI-powered Noise Cancellation in Podcast Production
5.5 Hands-on: Use Krisp or Adobe Enhance Speech to clean noisy audio
Module 6: Emotion & Sentiment Detection from Audio
6.1 Introduction to Emotion Detection
6.2 AI Models for Emotion Detection: RNNs, LSTMs, CNNs
6.3 Challenges: Bias, Multilingual Contexts, Reliability
6.4 Use Case: Enhancing Customer Service with Emotion Detection from Speech
6.5 Case Study: IBM Watson Tone Analyzer for Real-Time Emotion Recognition
6.6 Hands-on: Use IBM Watson Tone Analyzer or similar APIs to analyze speech samples
Module 7: Ethical and Privacy Considerations
7.1 Deepfakes and Voice Cloning Risks
7.2 Privacy and Data Security
7.3 Bias and Fairness in Audio AI
7.4 Use Case: Implementing Ethical Voice Data Collection and Consent Management
7.5 Case Study: Addressing Bias and Privacy in Audio AI under GDPR Compliance
7.6 Hands-on: Detect fake audio clips; create an ethical AI checklist
Module 8: Advanced Applications & Future Trends
8.1 Sound Event Detection & Classification
8.2 Audio Search and Indexing
8.3 Innovations: Multimodal AI, Edge Computing, 3D Audio
8.4 Emerging Careers in Audio AI
Tools you will explore
TensorFlow Audio Recognition
PyTorch Sound Classification
Librosa
OpenAI Jukebox
Google Magenta Studio
Audacity AI Plugins
Adobe Podcast AI Tools
AIVA
Wav2Vec
SpeechBrain
JUCE Framework
FL Studio with AI Integrations
Logic Pro Smart Tools
Sonible Smart EQ
Spotify Audio Analysis API
NVIDIA Riva Speech SDK
Deep Learning for Audio Toolkit
AudioLDM
Sound Design Automation Tools
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.
Included Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€225
E-Learning
max 999
1 dag
AI+ Video Practitioner™
Formerly known as AI+ Video™
Embrace the future of AI in video to inspire innovation and craft immersive visual experiences
Beginner-Friendly Pathway: A perfect starting point for learners exploring AI-driven video creation, editing, and automation
End-to-End Mastery: Covers AI video fundamentals, advanced tools, generative video workflows, and responsible content creation
Industry-Aligned Skills: Understand how AI video technologies shape marketing, education, entertainment, and business communication
Practical Execution: Provides guided exercises, templates, and workflows to help you produce professional-quality AI-powered videos confidently
Video Creators and Editors: Professionals looking to integrate AI tools to automate editing, enhance visuals, and speed up production.
Filmmakers and Producers: Those aiming to explore intelligent video generation, virtual production, and data-driven storytelling.
Content Creators and Influencers: Individuals seeking to elevate their visual content with AI-powered creativity and personalized video experiences.
Designers and Animators: Creatives who want to merge motion design with AI-driven effects and smart visual enhancements.
Tech Enthusiasts and Innovators: Learners eager to understand how AI is transforming the future of video creation and media technology.
Future-Proof Your Skills :Stay ahead in a rapidly evolving industry where AI-driven video creation is becoming the new standard.
Boost Creative Efficiency :Learn to automate editing, enhance visuals, and streamline production workflows using intelligent video tools.
Master Emerging Technologies :Gain hands-on experience with AI techniques shaping the future of visual storytelling and digital media.
Increase Career Opportunities :Stand out to employers and clients seeking professionals skilled in next-gen video innovation and automation.
Bridge Art and Technology :Combine creative vision with AI-powered precision to produce cinematic experiences that captivate and inspire.
Module 1: Foundation of AI in Video Integration
1.1 Basics of Video Processing
1.2 Introduction to AI in Video
1.3 Toolkits and Framework
1.4 Use Case: AI-enhanced Video Compression for Streaming Platforms
1.5 Case Study: YouTube’s AI-Driven Transcoding System
Module 2: Preparing Video Data for AI
2.1 Data Preparation for AI Models
2.2 Preprocessing and Augmenting Frames
2.3 Storage and Workflow Management
2.4 Use Case: Building AI-ready Video Datasets for Autonomous Driving Applications
2.5 Case Study: Tesla’s In-house Pipeline for Labeling Driving Scenarios across Multiple Geographies using Video Footage
2.6 Hands-On: Video Annotation using CVAT Tool, and Organizing them for Model Training
Module 3: Machine Learning for Video Analysis
3.1 Video Classification and Tagging
3.2 Object Detection and Movement Tracking
3.3 Action and Behavior Recognition
3.4 Use Case: Smart Surveillance Systems Detecting Abandoned Objects in Real Time
3.5 Case Study: Dubai Smart City’s AI Implementation for Object Recognition
3.6 Hands-On: Train YOLO on Sample Security Footage to Detect and Track Objects
Module 4: Generative AI in Video
4.1 Generating Synthetic Video with GANs
4.2 AI-Driven Animation and Avatars
4.3 Ethical Use of Generative Content
4.4 Use Case: Auto-Generation of Product Explainer Videos using Avatars and Synthesized Narration
4.5 Case Study: Synthesia’s Solution Enabling Businesses to Create AI-Driven Training and Marketing Videos
4.6 Hands-On: Generate a Deepfake or AI Avatar using AKOOL, and Explore Face Alignment and Identity Swapping
Module 5: Enhancing Video with AI
5.1 Super-Resolution and Restoration
5.2 Real-Time Video Enhancement
5.3 Making Video More Inclusive
5.4 Use Case: Streaming Platforms using AI to Enhance Resolution and Reduce Latency for Mobile Users.
5.5 Case Study: DeOldify’s Impact in Reviving Historical Video Archives by Upscaling and Colorizing Black-and-White Footage.
5.6 Hands-On: Use AI4Video to Enhance a Sample Low-Resolution Black-and-White Video and Visualize Improvement
Module 6: Interactive and Immersive AI Video
6.1 AI in AR and Mixed Reality
6.2 Intelligent Video Editing
6.3 Viewer Engagement & Adaptation
6.4 Use Case: Live Sports Broadcasters using AR to Overlay Player Stats during Gameplay
6.5 Case Study: NFL and AWS Collaboration to Deliver Real-Time Performance Insights via Augmented Visuals.
6.6 Hands-On: Creating a Highlight Video from a Video Clip using Clipchamp
Module 7: AI in Video Surveillance and Compliance
7.1 Security and Monitoring Systems
7.2 Automated Content Moderation
7.3 Addressing Privacy and Ethics
7.4 Use Case: Automated Real-Time Access Control in Corporate Offices Using Facial Authentication.
7.5 Case Study: Amazon Go’s Cashier-less Stores Using Computer Vision for Security and Consumer Behavior Tracking
7.6 Hands-On: Implement Facial Detection and Access Control Simulation using OpenCV and a Basic Recognition Model
Module 8: Future of AI+ Video Practitioner™
8.1 Trends and Emerging Technologies
8.2 AI Applications by Industry
8.3 Careers and Professional Growth
Tools you will explore
TensorFlow
PyTorch
OpenCV
MediaPipe
Runway ML
Synthesia Studio
DeepFaceLab
Adobe Sensei
DaVinci Resolve Neural Engine
Runway
Pika Labs
Kaiber AI
DeepBrain AI Studio
NVIDIA Maxine SDK
Google Video AI API
FFmpeg Automation Tools
Unreal Engine with AI Plugins
Blender AI Add-ons
Stability Video Diffusion
Generative Video Editing Tools
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€995
Klassikaal
max 12
1 dag
AI+ Video Practitioner™ eLearning
Formerly known as AI+ Video™
Embrace the future of AI in video to inspire innovation and craft immersive visual experiences
Beginner-Friendly Pathway: A perfect starting point for learners exploring AI-driven video creation, editing, and automation
End-to-End Mastery: Covers AI video fundamentals, advanced tools, generative video workflows, and responsible content creation
Industry-Aligned Skills: Understand how AI video technologies shape marketing, education, entertainment, and business communication
Practical Execution: Provides guided exercises, templates, and workflows to help you produce professional-quality AI-powered videos confidently
Video Creators and Editors: Professionals looking to integrate AI tools to automate editing, enhance visuals, and speed up production.
Filmmakers and Producers: Those aiming to explore intelligent video generation, virtual production, and data-driven storytelling.
Content Creators and Influencers: Individuals seeking to elevate their visual content with AI-powered creativity and personalized video experiences.
Designers and Animators: Creatives who want to merge motion design with AI-driven effects and smart visual enhancements.
Tech Enthusiasts and Innovators: Learners eager to understand how AI is transforming the future of video creation and media technology.
Future-Proof Your Skills :Stay ahead in a rapidly evolving industry where AI-driven video creation is becoming the new standard.
Boost Creative Efficiency :Learn to automate editing, enhance visuals, and streamline production workflows using intelligent video tools.
Master Emerging Technologies :Gain hands-on experience with AI techniques shaping the future of visual storytelling and digital media.
Increase Career Opportunities :Stand out to employers and clients seeking professionals skilled in next-gen video innovation and automation.
Bridge Art and Technology :Combine creative vision with AI-powered precision to produce cinematic experiences that captivate and inspire.
Module 1: Foundation of AI in Video Integration
1.1 Basics of Video Processing
1.2 Introduction to AI in Video
1.3 Toolkits and Framework
1.4 Use Case: AI-enhanced Video Compression for Streaming Platforms
1.5 Case Study: YouTube’s AI-Driven Transcoding System
Module 2: Preparing Video Data for AI
2.1 Data Preparation for AI Models
2.2 Preprocessing and Augmenting Frames
2.3 Storage and Workflow Management
2.4 Use Case: Building AI-ready Video Datasets for Autonomous Driving Applications
2.5 Case Study: Tesla’s In-house Pipeline for Labeling Driving Scenarios across Multiple Geographies using Video Footage
2.6 Hands-On: Video Annotation using CVAT Tool, and Organizing them for Model Training
Module 3: Machine Learning for Video Analysis
3.1 Video Classification and Tagging
3.2 Object Detection and Movement Tracking
3.3 Action and Behavior Recognition
3.4 Use Case: Smart Surveillance Systems Detecting Abandoned Objects in Real Time
3.5 Case Study: Dubai Smart City’s AI Implementation for Object Recognition
3.6 Hands-On: Train YOLO on Sample Security Footage to Detect and Track Objects
Module 4: Generative AI in Video
4.1 Generating Synthetic Video with GANs
4.2 AI-Driven Animation and Avatars
4.3 Ethical Use of Generative Content
4.4 Use Case: Auto-Generation of Product Explainer Videos using Avatars and Synthesized Narration
4.5 Case Study: Synthesia’s Solution Enabling Businesses to Create AI-Driven Training and Marketing Videos
4.6 Hands-On: Generate a Deepfake or AI Avatar using AKOOL, and Explore Face Alignment and Identity Swapping
Module 5: Enhancing Video with AI
5.1 Super-Resolution and Restoration
5.2 Real-Time Video Enhancement
5.3 Making Video More Inclusive
5.4 Use Case: Streaming Platforms using AI to Enhance Resolution and Reduce Latency for Mobile Users.
5.5 Case Study: DeOldify’s Impact in Reviving Historical Video Archives by Upscaling and Colorizing Black-and-White Footage.
5.6 Hands-On: Use AI4Video to Enhance a Sample Low-Resolution Black-and-White Video and Visualize Improvement
Module 6: Interactive and Immersive AI Video
6.1 AI in AR and Mixed Reality
6.2 Intelligent Video Editing
6.3 Viewer Engagement & Adaptation
6.4 Use Case: Live Sports Broadcasters using AR to Overlay Player Stats during Gameplay
6.5 Case Study: NFL and AWS Collaboration to Deliver Real-Time Performance Insights via Augmented Visuals.
6.6 Hands-On: Creating a Highlight Video from a Video Clip using Clipchamp
Module 7: AI in Video Surveillance and Compliance
7.1 Security and Monitoring Systems
7.2 Automated Content Moderation
7.3 Addressing Privacy and Ethics
7.4 Use Case: Automated Real-Time Access Control in Corporate Offices Using Facial Authentication.
7.5 Case Study: Amazon Go’s Cashier-less Stores Using Computer Vision for Security and Consumer Behavior Tracking
7.6 Hands-On: Implement Facial Detection and Access Control Simulation using OpenCV and a Basic Recognition Model
Module 8: Future of AI+ Video Practitioner™
8.1 Trends and Emerging Technologies
8.2 AI Applications by Industry
8.3 Careers and Professional Growth
Tools you will explore
TensorFlow
PyTorch
OpenCV
MediaPipe
Runway ML
Synthesia Studio
DeepFaceLab
Adobe Sensei
DaVinci Resolve Neural Engine
Runway
Pika Labs
Kaiber AI
DeepBrain AI Studio
NVIDIA Maxine SDK
Google Video AI API
FFmpeg Automation Tools
Unreal Engine with AI Plugins
Blender AI Add-ons
Stability Video Diffusion
Generative Video Editing Tools
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€225
E-Learning
max 999
1 dag
AI+ Supply Chain Practitioner™
Formerly known as AI+ Supply Chain™
Transforming Supply Chain Management
Comprehensive Learning: Covers logistics, operations, and supply chain digitization
Advanced Supply Strategies: Develop innovative supply strategies and workflows
Sector-Specific Solutions: Tailored sessions for real-world, sector-specific challenges
Lead AI Supply Efficiency: Prepares learners to lead in AI-led supply chain efficiency
Supply Chain Professionals: Enhance your supply chain management skills by integrating AI tools for improved forecasting, inventory management, and process optimization.
Logistics & Operations Managers: Learn to leverage AI for optimizing logistics, route planning, and warehouse management to enhance operational efficiency and reduce costs.
Procurement Experts: Use AI to improve supplier selection, demand forecasting, and inventory replenishment, streamlining procurement processes.
Business Leaders: Drive innovation in your supply chain by adopting AI technologies to automate workflows, predict demand, and optimize end-to-end operations.
Students & New Graduates: Gain a competitive advantage in the supply chain field by mastering AI tools and techniques that are transforming global logistics and supply chain management.
Leverage AI for Smarter Supply Chain Operations:Learn how AI tools can optimize logistics, reduce costs, and improve supply chain efficiency from end to end.
Optimize Demand Forecasting with AI:Use AI-driven analytics to predict demand, streamline inventory management, and reduce stockouts or overstocking.
Stay Ahead in AI-Powered Supply Chain:As industries increasingly adopt AI, professionals with AI supply chain expertise are in high demand to drive innovation and efficiency.
Enhance Decision-Making with Predictive Analytics:Master AI algorithms to analyze supply chain data and make informed, real-time decisions to improve overall operations.
Module 1: Fundamental Concepts of Supply Chain Management
1.1 SCOR Model and Core Processes (Plan, Source, Make, Deliver, Return, Enable)
1.2 Key Functions: Procurement, Inventory Management, Logistics, Warehousing, Demand Forecasting, Risk, and Resilience
1.3 Global Challenges: Volatility, Sustainability, Nearshoring, and ESG
1.4 KPIs and Performance Measurement
1.5 Activity: Analyze and Map a Real-World Supply Chain
Module 2: AI Concepts, Techniques, and Tools for SCM
2.1 AI/ML Fundamentals – Supervised & Unsupervised Learning, Predictive & Prescriptive Analytics, Optimization, Reinforcement Learning
2.2 Key Techniques – Neural Networks, Computer Vision, NLP, Digital Twins, Edge AI
2.3 AI Tools for SCM
2.4 Data Foundations – IoT, Real-Time Data Pipelines, Data Quality & Governance
Module 3: LLM and Generative AI Applications in SCM
3.1 LLM/GenAI Fundamentals and Enterprise Integration
3.2 Use Cases – Demand Planning Assistance, Contract Analysis, Supplier Communication, Scenario Simulation, Report Generation, Synthetic Data
3.3 Chat-Based Copilots for Planners and Knowledge Management
3.4 Limitations and Best Practices (Hallucinations, Grounding, Integration)
3.5 Tools – Enterprise GPT-like Models, LangChain/LlamaIndex, Amazon Business Assistant, Custom GenAI Workflows
Module 4: Ethical Considerations and Responsible AI in SCM
4.1 Bias in Forecasting/Procurement, Transparency, and Explainability
4.2 Privacy, Security, Regulatory Compliance
4.3 Job Displacement, Upskilling, and Human-AI Collaboration
4.4 Sustainability & ESG – AI for Ethical Sourcing and Carbon Tracking
4.5 Governance Frameworks and Risk Management
Module 5: Supply Chain Digitization, Orchestration, and Intelligent Systems
5.1 Digitization – ERP + SCM Platforms, Cloud Integration, Blockchain for Traceability, APIs
5.2 Orchestration – Control Towers, Real-Time Visibility, Data Pipelines, Digital Twins
5.3 Intelligent & Smart SCM – Predictive/Prescriptive Analytics, Autonomous Exception Management, Robotics + Computer Vision, Edge AI
5.4 Human + AI Collaboration Models
Module 6: Industrial Applications, Case Studies, and Business Value
6.1 Applications Across Industries
6.2 Real-World ROI – Efficiency Gains, Cost Reduction, and Resilience Improvements
6.3 Implementation Best Practices
6.4 Case Studies from Blue Yonder, Kinaxis, Oracle, and Others
Module 7: Strategic SCM, Logistics Policies, and Sustainability
7.1 Logistics Policies, Trade Regulations, Tariffs, and Geopolitical Risks
7.2 Strategic Network Design: Optimization, Resilience, Nearshoring, and Friendshoring
7.3 Sustainable SCM: Circular Economy, Green Logistics, and AI-Driven ESG Reporting
7.4 Organizational Transformation and Leadership in AI-Enabled Supply Chains
7.5 Case Studies
Module 8: Agentic AI and the Future of Autonomous Supply Chains
8.1 Agentic AI Concepts: Autonomous Goal-Oriented Agents, Multi-Agent Systems, and Reasoning-Action Loops
8.2 Applications: Autonomous Replenishment, Risk Mitigation, Supplier Onboarding, Dynamic Rerouting, and End-to-End Orchestration
8.3 Tools & Platforms: Kinaxis Maestro Agents, Oracle AI Agents, Blue Yonder Cognitive Agents, Custom Builds, and Automation Anywhere
8.4 Architectures, Guardrails, and Human Oversight
8.5 Future Outlook for 2026+: From Copilots to Semi-Autonomous Operations
8.6 Capstone Project: Design and Prototype a Multi-Agent Workflow for a Supply Chain
8.7 Case Studies
Optional Module: AI Agents in Supply Chain
1. What Are AI Agents
2. What Are AI Agents in Logistics and Supply Chain
3. Applications & Trends of AI Agents in Supply Chain
4. How Does an AI Agent Work
5. Core Characteristics of AI Agents
6. Key Advantages of AI Agents in Logistics and Supply Chain
7. Types of AI Agent
8. Case Studies
9. Hands on experiment
Tools you will explore
LeewayHertz (ZBrain)
C3.ai
Coupa (LLamasoft)
Zebra (Workcloud Demand Intelligence Suite)
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€995
Klassikaal
max 12
1 dag
AI+ Supply Chain Practitioner™ eLearning
Formerly known as AI+ Supply Chain™
Transforming Supply Chain Management
Comprehensive Learning: Covers logistics, operations, and supply chain digitization
Advanced Supply Strategies: Develop innovative supply strategies and workflows
Sector-Specific Solutions: Tailored sessions for real-world, sector-specific challenges
Lead AI Supply Efficiency: Prepares learners to lead in AI-led supply chain efficiency
Supply Chain Professionals: Enhance your supply chain management skills by integrating AI tools for improved forecasting, inventory management, and process optimization.
Logistics & Operations Managers: Learn to leverage AI for optimizing logistics, route planning, and warehouse management to enhance operational efficiency and reduce costs.
Procurement Experts: Use AI to improve supplier selection, demand forecasting, and inventory replenishment, streamlining procurement processes.
Business Leaders: Drive innovation in your supply chain by adopting AI technologies to automate workflows, predict demand, and optimize end-to-end operations.
Students & New Graduates: Gain a competitive advantage in the supply chain field by mastering AI tools and techniques that are transforming global logistics and supply chain management.
Leverage AI for Smarter Supply Chain Operations:Learn how AI tools can optimize logistics, reduce costs, and improve supply chain efficiency from end to end.
Optimize Demand Forecasting with AI:Use AI-driven analytics to predict demand, streamline inventory management, and reduce stockouts or overstocking.
Stay Ahead in AI-Powered Supply Chain:As industries increasingly adopt AI, professionals with AI supply chain expertise are in high demand to drive innovation and efficiency.
Enhance Decision-Making with Predictive Analytics:Master AI algorithms to analyze supply chain data and make informed, real-time decisions to improve overall operations.
Module 1: Fundamental Concepts of Supply Chain Management
1.1 SCOR Model and Core Processes (Plan, Source, Make, Deliver, Return, Enable)
1.2 Key Functions: Procurement, Inventory Management, Logistics, Warehousing, Demand Forecasting, Risk, and Resilience
1.3 Global Challenges: Volatility, Sustainability, Nearshoring, and ESG
1.4 KPIs and Performance Measurement
1.5 Activity: Analyze and Map a Real-World Supply Chain
Module 2: AI Concepts, Techniques, and Tools for SCM
2.1 AI/ML Fundamentals – Supervised & Unsupervised Learning, Predictive & Prescriptive Analytics, Optimization, Reinforcement Learning
2.2 Key Techniques – Neural Networks, Computer Vision, NLP, Digital Twins, Edge AI
2.3 AI Tools for SCM
2.4 Data Foundations – IoT, Real-Time Data Pipelines, Data Quality & Governance
Module 3: LLM and Generative AI Applications in SCM
3.1 LLM/GenAI Fundamentals and Enterprise Integration
3.2 Use Cases – Demand Planning Assistance, Contract Analysis, Supplier Communication, Scenario Simulation, Report Generation, Synthetic Data
3.3 Chat-Based Copilots for Planners and Knowledge Management
3.4 Limitations and Best Practices (Hallucinations, Grounding, Integration)
3.5 Tools – Enterprise GPT-like Models, LangChain/LlamaIndex, Amazon Business Assistant, Custom GenAI Workflows
Module 4: Ethical Considerations and Responsible AI in SCM
4.1 Bias in Forecasting/Procurement, Transparency, and Explainability
4.2 Privacy, Security, Regulatory Compliance
4.3 Job Displacement, Upskilling, and Human-AI Collaboration
4.4 Sustainability & ESG – AI for Ethical Sourcing and Carbon Tracking
4.5 Governance Frameworks and Risk Management
Module 5: Supply Chain Digitization, Orchestration, and Intelligent Systems
5.1 Digitization – ERP + SCM Platforms, Cloud Integration, Blockchain for Traceability, APIs
5.2 Orchestration – Control Towers, Real-Time Visibility, Data Pipelines, Digital Twins
5.3 Intelligent & Smart SCM – Predictive/Prescriptive Analytics, Autonomous Exception Management, Robotics + Computer Vision, Edge AI
5.4 Human + AI Collaboration Models
Module 6: Industrial Applications, Case Studies, and Business Value
6.1 Applications Across Industries
6.2 Real-World ROI – Efficiency Gains, Cost Reduction, and Resilience Improvements
6.3 Implementation Best Practices
6.4 Case Studies from Blue Yonder, Kinaxis, Oracle, and Others
Module 7: Strategic SCM, Logistics Policies, and Sustainability
7.1 Logistics Policies, Trade Regulations, Tariffs, and Geopolitical Risks
7.2 Strategic Network Design: Optimization, Resilience, Nearshoring, and Friendshoring
7.3 Sustainable SCM: Circular Economy, Green Logistics, and AI-Driven ESG Reporting
7.4 Organizational Transformation and Leadership in AI-Enabled Supply Chains
7.5 Case Studies
Module 8: Agentic AI and the Future of Autonomous Supply Chains
8.1 Agentic AI Concepts: Autonomous Goal-Oriented Agents, Multi-Agent Systems, and Reasoning-Action Loops
8.2 Applications: Autonomous Replenishment, Risk Mitigation, Supplier Onboarding, Dynamic Rerouting, and End-to-End Orchestration
8.3 Tools & Platforms: Kinaxis Maestro Agents, Oracle AI Agents, Blue Yonder Cognitive Agents, Custom Builds, and Automation Anywhere
8.4 Architectures, Guardrails, and Human Oversight
8.5 Future Outlook for 2026+: From Copilots to Semi-Autonomous Operations
8.6 Capstone Project: Design and Prototype a Multi-Agent Workflow for a Supply Chain
8.7 Case Studies
Optional Module: AI Agents in Supply Chain
1. What Are AI Agents
2. What Are AI Agents in Logistics and Supply Chain
3. Applications & Trends of AI Agents in Supply Chain
4. How Does an AI Agent Work
5. Core Characteristics of AI Agents
6. Key Advantages of AI Agents in Logistics and Supply Chain
7. Types of AI Agent
8. Case Studies
9. Hands on experiment
Tools you will explore
LeewayHertz (ZBrain)
C3.ai
Coupa (LLamasoft)
Zebra (Workcloud Demand Intelligence Suite)
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€225
E-Learning
max 999
1 dag
AI+ Chief AI Officer Practitioner™
Formerly known as AI+ Chief AI Officer™
AI Leadership for Chief Officers: Driving Innovation and Intelligence
Leadership Upgrade: Equip C-suite executives to lead AI-driven innovation
Efficiency Focus: Use AI tools to optimize operations, decision-making, and resources
Strategic Role: Aligns AI implementation with business intelligence goals
Course + Exam: Combines theory and practical insights in a compact format
Current CTOs, CIOs, or CDOs (Chief Digital Officers): Senior technology executives who are responsible for overseeing AI adoption.
CEOs and Founders of Tech Companies: Entrepreneurs or CEOs of startups and tech companies who want to integrate AI technologies into their strategic vision.
COOs and Operations Executives: Those responsible for the day-to-day operations of a business who seek to leverage AI to optimize operations.
Students & New Graduates: Gain a competitive edge in the Chief AI Officer role by mastering AI tools and strategies that is driving leadership in AI innovation.
Drive AI Strategy:Learn to develop and execute a strategic AI roadmap to guide organizational growth and innovation.
Manage Cybersecurity Risks:Gain insights into mitigating cybersecurity threats and ensuring AI systems' security and privacy.
Foster Data-Driven Decisions:Master techniques to utilize data effectively for informed decision-making and business optimization.
Build High-Performing Teams:Learn strategies to assemble and lead AI-focused teams that drive successful project execution.
Navigate Regulatory Frameworks:Understand the regulatory environment around AI and ensure compliance while maximizing business impact.
Module 1: Foundations of AI and Leadership in the Digital Era
1.1 Defining Artificial Intelligence
1.2 Key AI Technologies
1.3 The CAIO’s Unique Role
1.4 Navigating Cybersecurity Challenges
1.5 Establishing Cross-Departmental Collaboration
1.6 Case Study
Module 2: Crafting a Strategic AI Roadmap
2.1 Aligning AI with Business Objectives
2.2 Setting Measurable Goals
2.3 Identifying Opportunities for Innovation
2.4 Engaging Stakeholders Across Departments
2.5 Monitoring Progress and Adjusting Plans
2.6 Case Study
Module 3: Building a High-Performance AI Team
3.1 Key Roles in an AI Team
3.2 Recruitment Strategies for Top Talent
3.3 Cultivating a Collaborative Culture
3.4 Continuous Learning Initiatives
3.5 Evaluating Team Performance
3.6 Case Study
Module 4: Ethics in AI Governance and Risk Management
4.1 Integrating Ethical Frameworks into AI Development
4.2 Conducting Ethical Impact Assessments
4.3 Developing Risk Mitigation Strategies
4.4 Establishing Transparency Protocols
4.5 AI Governance Models and Frameworks
4.6 Case Study
Module 5: Data-Driven Decision-Making and Business Impact Assessment
5.1 The Role of Data in AI Initiatives
5.2 Business Impact Assessment Frameworks
5.3 Measuring ROI from AI Investments
5.4 Hypothesis Testing in AI Projects
5.5 Resource Allocation Strategies
5.6 Case Study
Module 6: Driving Organization-Wide Adoption of AI
6.1 Creating Change Management Strategies
6.2 Communicating the Value of AI Initiatives
6.3 Addressing Resistance to Change
6.4 Metrics for Success Evaluation
6.5 Case Study
Module 7: Leveraging Generative AI for Business Innovation
7.1 Understanding Generative AI Capabilities
7.2 Identifying Areas for Innovation with Generative AI
7.3 Integrating Generative Solutions into Business Processes
7.4 Managing Risks Associated with Generative Applications
7.5 Creating Interdepartmental Synergies with Generative AI
7.6 Case Study
Module 8: Capstone Project
8.1 Project Overview and Objectives
8.2 Collaborative Work Sessions
8.3 Presentation Skills Workshop
8.4 Final Presentations and Constructive Feedback
8.5 Reflection on Key Takeaways from the Course Experience
Optional Module: AI Agents for Chief AI Officer
1. What Are AI Agents
2. Key Capabilities of AI Agents for the Chief AI Officer
3. Applications and Trends of AI Agents for the Chief AI Officer
4. How Does an AI Agent Work
5. Core Characteristics of AI Agents
6. Types of AI Agents
Tools you will explore
LeewayHertz (ZBrain)
C3.ai
Coupa (LLamasoft)
Zebra (Workcloud Demand Intelligence Suite)
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€995
Klassikaal
max 12
1 dag
AI+ Chief AI Officer Practitioner™ eLearning
Formerly known as AI+ Chief AI Officer™
AI Leadership for Chief Officers: Driving Innovation and Intelligence
Leadership Upgrade: Equip C-suite executives to lead AI-driven innovation
Efficiency Focus: Use AI tools to optimize operations, decision-making, and resources
Strategic Role: Aligns AI implementation with business intelligence goals
Course + Exam: Combines theory and practical insights in a compact format
Current CTOs, CIOs, or CDOs (Chief Digital Officers): Senior technology executives who are responsible for overseeing AI adoption.
CEOs and Founders of Tech Companies: Entrepreneurs or CEOs of startups and tech companies who want to integrate AI technologies into their strategic vision.
COOs and Operations Executives: Those responsible for the day-to-day operations of a business who seek to leverage AI to optimize operations.
Students & New Graduates: Gain a competitive edge in the Chief AI Officer role by mastering AI tools and strategies that is driving leadership in AI innovation.
Drive AI Strategy:Learn to develop and execute a strategic AI roadmap to guide organizational growth and innovation.
Manage Cybersecurity Risks:Gain insights into mitigating cybersecurity threats and ensuring AI systems' security and privacy.
Foster Data-Driven Decisions:Master techniques to utilize data effectively for informed decision-making and business optimization.
Build High-Performing Teams:Learn strategies to assemble and lead AI-focused teams that drive successful project execution.
Navigate Regulatory Frameworks:Understand the regulatory environment around AI and ensure compliance while maximizing business impact.
Module 1: Foundations of AI and Leadership in the Digital Era
1.1 Defining Artificial Intelligence
1.2 Key AI Technologies
1.3 The CAIO’s Unique Role
1.4 Navigating Cybersecurity Challenges
1.5 Establishing Cross-Departmental Collaboration
1.6 Case Study
Module 2: Crafting a Strategic AI Roadmap
2.1 Aligning AI with Business Objectives
2.2 Setting Measurable Goals
2.3 Identifying Opportunities for Innovation
2.4 Engaging Stakeholders Across Departments
2.5 Monitoring Progress and Adjusting Plans
2.6 Case Study
Module 3: Building a High-Performance AI Team
3.1 Key Roles in an AI Team
3.2 Recruitment Strategies for Top Talent
3.3 Cultivating a Collaborative Culture
3.4 Continuous Learning Initiatives
3.5 Evaluating Team Performance
3.6 Case Study
Module 4: Ethics in AI Governance and Risk Management
4.1 Integrating Ethical Frameworks into AI Development
4.2 Conducting Ethical Impact Assessments
4.3 Developing Risk Mitigation Strategies
4.4 Establishing Transparency Protocols
4.5 AI Governance Models and Frameworks
4.6 Case Study
Module 5: Data-Driven Decision-Making and Business Impact Assessment
5.1 The Role of Data in AI Initiatives
5.2 Business Impact Assessment Frameworks
5.3 Measuring ROI from AI Investments
5.4 Hypothesis Testing in AI Projects
5.5 Resource Allocation Strategies
5.6 Case Study
Module 6: Driving Organization-Wide Adoption of AI
6.1 Creating Change Management Strategies
6.2 Communicating the Value of AI Initiatives
6.3 Addressing Resistance to Change
6.4 Metrics for Success Evaluation
6.5 Case Study
Module 7: Leveraging Generative AI for Business Innovation
7.1 Understanding Generative AI Capabilities
7.2 Identifying Areas for Innovation with Generative AI
7.3 Integrating Generative Solutions into Business Processes
7.4 Managing Risks Associated with Generative Applications
7.5 Creating Interdepartmental Synergies with Generative AI
7.6 Case Study
Module 8: Capstone Project
8.1 Project Overview and Objectives
8.2 Collaborative Work Sessions
8.3 Presentation Skills Workshop
8.4 Final Presentations and Constructive Feedback
8.5 Reflection on Key Takeaways from the Course Experience
Optional Module: AI Agents for Chief AI Officer
1. What Are AI Agents
2. Key Capabilities of AI Agents for the Chief AI Officer
3. Applications and Trends of AI Agents for the Chief AI Officer
4. How Does an AI Agent Work
5. Core Characteristics of AI Agents
6. Types of AI Agents
Tools you will explore
LeewayHertz (ZBrain)
C3.ai
Coupa (LLamasoft)
Zebra (Workcloud Demand Intelligence Suite)
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€225
E-Learning
max 999
1 dag
AI+ Sustainability Practitioner™
Formerly known as AI+ Sustainability™
Accelerate Sustainability with AI for smarter, greener progress
Drive Sustainable Innovation: Harness the Power of Advanced AI
AI for Greener Decisions: Explore carbon footprint analytics, resource optimization, and climate-impact modelling.
Strategic Sustainability Impact: Learn to design data-driven, eco-focused frameworks that support long-term environmental goals.
Future-Ready Tools: Includes lifecycle assessment tools, emission-tracking AI, and smart energy-management systems.
Efficiency & Responsibility: Boost operational efficiency, reduce waste, and accelerate your organization’s journey towards a cleaner, climate-conscious future.
Environmental Enthusiasts: Those passionate about leveraging technology to tackle global sustainability challenges and drive positive change.
Data Analysts: Professionals looking to apply their data analytics skills to sustainability efforts and environmental decision-making.
Tech Innovators: Individuals interested in using AI to develop innovative solutions for resource optimization and environmental conservation.
Environmental Scientists: Experts aiming to integrate AI technologies into their research and sustainability projects for greater impact.
Business Leaders: Managers seeking to implement AI-driven sustainable practices and strategies within their organizations.
Sustainable Innovation:AI enables the development of eco-friendly technologies, addressing environmental challenges with advanced solutions.
Resource Efficiency:AI helps optimize energy consumption, waste management, and resource distribution, minimizing environmental impact.
Data-Driven Insights:AI analyzes large datasets to inform sustainability strategies, providing accurate predictions and actionable solutions.
Green Business Practices:With AI knowledge, businesses can adopt environmentally conscious practices, meeting the demand for sustainability.
Preparation for Future Challenges:AI expertise equips professionals to lead in tackling the growing environmental issues and sustainability needs.
Module 1: Introduction to AI and Sustainability
1.1 Overview of Artificial Intelligence
1.2 Introduction to Sustainability
1.3 Sustainability Challenges
1.4 AI for Green
1.5 Case Study: AI Models for Climate Change Prediction
1.6 Hands On: Visualizing Global CO₂ Emissions Trends with GPT
Module 2: AI Techniques for Sustainability Solutions
2.1 Introduction to Machine Learning for Sustainability
2.2 Supervised Learning for Environmental Impact
2.3 Unsupervised Learning for Environmental Insights
2.4 Reinforcement Learning for Sustainable Systems
2.5 Green AI: Sustainable AI Models
2.6 Hands-On
Module 3: AI for Climate Change Mitigation
3.1 AI in Climate Modeling
3.2 AI for Renewable Energy Integration
3.3 Carbon Footprint Reduction
3.4 Case Study: Optimizing Wind Turbine Operations with AI
3.5 Hands-On Exercises
Module 4: AI in Sustainable Energy Systems
4.1 AI for Energy Optimization
4.2 Renewable Energy Integration
4.3 AI in Energy Storage and Efficiency
4.4 Case Study: AI-Powered Smart Grids: Optimizing Energy Distribution and Integrating Renewables
4.5 Hands-On Exercises: Optimizing Smart Grid Load Balancing
Module 5: AI for Sustainable Agriculture
5.1 Precision Agriculture and Resource Optimization
5.2 AI for Pest and Disease Detection
5.3 Sustainable Farming and Decision Support Systems
5.4 Case Study: AI in Precision Agriculture
5.5 Hands-On: Predicting Crop Yields with Machine Learning
Module 6: AI in Waste Management and Circular Economy
6.1 AI for Waste Sorting and Recycling
6.2 AI for Waste-to-Energy Solutions
6.3 Circular Economy and Resource Recovery
6.4 Case Study: AI for Waste Sorting and Recycling
6.5 Hands-On: Building a Waste Sorting Classifier with AI
Module 7: AI for Biodiversity Conservation and Environmental Monitoring
7.1 AI in Remote Sensing for Environmental Monitoring
7.2 Wildlife Tracking and Conservation
7.3 AI for Ecosystem Health Monitoring
7.4 Case Study: AI for Deforestation Monitoring
7.5 Hands-On: Detecting Deforestation Using Satellite Imagery
Module 8: AI for Water Resource Management
8.1 AI for Water Consumption Prediction
8.2 AI for Smart Irrigation Systems
8.3 Water Quality Monitoring and Analysis
8.4 Case Study: AI for Smart Irrigation Systems
8.5 Hands-On: Optimizing Irrigation Systems with AI
Module 9: AI for Sustainable Cities and Smart Urban Development
9.1 AI in Smart City Infrastructure
9.2 Sustainable Mobility and Transportation
9.3 AI in Urban Resource Optimization
9.4 Case Study: AI for Urban Air Quality Monitoring
9.5 Hands-On: Optimizing Traffic Flow and Reducing Emissions with AI-Driven Smart Traffic Management
Tools you will explore
TensorFlow
PyTorch
Python
Climate Prediction
AI-Driven Energy Management Systems
AI-Based Resource Optimization Tools
Machine Learning for Waste Reduction
Smart Grid Optimization Software
Environmental Data Visualization Platforms
Sustainability Analytics Frameworks
AI for Biodiversity Conservation
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€995
Klassikaal
max 12
1 dag
AI+ Sustainability Practitioner™ eLearning
Formerly known as AI+ Sustainability™
Accelerate Sustainability with AI for smarter, greener progress
Drive Sustainable Innovation: Harness the Power of Advanced AI
AI for Greener Decisions: Explore carbon footprint analytics, resource optimization, and climate-impact modelling.
Strategic Sustainability Impact: Learn to design data-driven, eco-focused frameworks that support long-term environmental goals.
Future-Ready Tools: Includes lifecycle assessment tools, emission-tracking AI, and smart energy-management systems.
Efficiency & Responsibility: Boost operational efficiency, reduce waste, and accelerate your organization’s journey towards a cleaner, climate-conscious future.
Environmental Enthusiasts: Those passionate about leveraging technology to tackle global sustainability challenges and drive positive change.
Data Analysts: Professionals looking to apply their data analytics skills to sustainability efforts and environmental decision-making.
Tech Innovators: Individuals interested in using AI to develop innovative solutions for resource optimization and environmental conservation.
Environmental Scientists: Experts aiming to integrate AI technologies into their research and sustainability projects for greater impact.
Business Leaders: Managers seeking to implement AI-driven sustainable practices and strategies within their organizations.
Sustainable Innovation:AI enables the development of eco-friendly technologies, addressing environmental challenges with advanced solutions.
Resource Efficiency:AI helps optimize energy consumption, waste management, and resource distribution, minimizing environmental impact.
Data-Driven Insights:AI analyzes large datasets to inform sustainability strategies, providing accurate predictions and actionable solutions.
Green Business Practices:With AI knowledge, businesses can adopt environmentally conscious practices, meeting the demand for sustainability.
Preparation for Future Challenges:AI expertise equips professionals to lead in tackling the growing environmental issues and sustainability needs.
Module 1: Introduction to AI and Sustainability
1.1 Overview of Artificial Intelligence
1.2 Introduction to Sustainability
1.3 Sustainability Challenges
1.4 AI for Green
1.5 Case Study: AI Models for Climate Change Prediction
1.6 Hands On: Visualizing Global CO₂ Emissions Trends with GPT
Module 2: AI Techniques for Sustainability Solutions
2.1 Introduction to Machine Learning for Sustainability
2.2 Supervised Learning for Environmental Impact
2.3 Unsupervised Learning for Environmental Insights
2.4 Reinforcement Learning for Sustainable Systems
2.5 Green AI: Sustainable AI Models
2.6 Hands-On
Module 3: AI for Climate Change Mitigation
3.1 AI in Climate Modeling
3.2 AI for Renewable Energy Integration
3.3 Carbon Footprint Reduction
3.4 Case Study: Optimizing Wind Turbine Operations with AI
3.5 Hands-On Exercises
Module 4: AI in Sustainable Energy Systems
4.1 AI for Energy Optimization
4.2 Renewable Energy Integration
4.3 AI in Energy Storage and Efficiency
4.4 Case Study: AI-Powered Smart Grids: Optimizing Energy Distribution and Integrating Renewables
4.5 Hands-On Exercises: Optimizing Smart Grid Load Balancing
Module 5: AI for Sustainable Agriculture
5.1 Precision Agriculture and Resource Optimization
5.2 AI for Pest and Disease Detection
5.3 Sustainable Farming and Decision Support Systems
5.4 Case Study: AI in Precision Agriculture
5.5 Hands-On: Predicting Crop Yields with Machine Learning
Module 6: AI in Waste Management and Circular Economy
6.1 AI for Waste Sorting and Recycling
6.2 AI for Waste-to-Energy Solutions
6.3 Circular Economy and Resource Recovery
6.4 Case Study: AI for Waste Sorting and Recycling
6.5 Hands-On: Building a Waste Sorting Classifier with AI
Module 7: AI for Biodiversity Conservation and Environmental Monitoring
7.1 AI in Remote Sensing for Environmental Monitoring
7.2 Wildlife Tracking and Conservation
7.3 AI for Ecosystem Health Monitoring
7.4 Case Study: AI for Deforestation Monitoring
7.5 Hands-On: Detecting Deforestation Using Satellite Imagery
Module 8: AI for Water Resource Management
8.1 AI for Water Consumption Prediction
8.2 AI for Smart Irrigation Systems
8.3 Water Quality Monitoring and Analysis
8.4 Case Study: AI for Smart Irrigation Systems
8.5 Hands-On: Optimizing Irrigation Systems with AI
Module 9: AI for Sustainable Cities and Smart Urban Development
9.1 AI in Smart City Infrastructure
9.2 Sustainable Mobility and Transportation
9.3 AI in Urban Resource Optimization
9.4 Case Study: AI for Urban Air Quality Monitoring
9.5 Hands-On: Optimizing Traffic Flow and Reducing Emissions with AI-Driven Smart Traffic Management
Tools you will explore
TensorFlow
PyTorch
Python
Climate Prediction
AI-Driven Energy Management Systems
AI-Based Resource Optimization Tools
Machine Learning for Waste Reduction
Smart Grid Optimization Software
Environmental Data Visualization Platforms
Sustainability Analytics Frameworks
AI for Biodiversity Conservation
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€225
E-Learning
max 999
1 dag
AI+ Cloud Practitioner™
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.
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
Tools you will explore
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.
Instructor-led OR Self-paced course + Official exam + Digital badge
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.
€3.930
Klassikaal
max 12
5 dagen