The Bigger Picture for AI in Saudi Arabia
Saudi Arabia is undergoing a transformative shift toward becoming a global hub for artificial intelligence and digital innovation. With initiatives like Vision 2030, the Kingdom has made AI a national priority—investing heavily in smart infrastructure, AI education, and digital transformation across sectors. The government’s commitment is evident in the establishment of bodies such as the Saudi Data and Artificial Intelligence Authority (SDAIA), which are actively laying the foundation for a robust AI ecosystem that aligns with economic diversification goals.
This Professional Generative AI Course arrives at a critical moment for Saudi professionals and enterprises. As generative AI reshapes industries—from automating customer service to accelerating innovation in healthcare, design, and finance—the need for advanced, hands-on training becomes vital. This course not only builds foundational understanding of AI models and systems but also equips learners with the skills to deploy them in real-world Saudi business contexts. Whether in public sector reform, smart city development, or entrepreneurial ventures, mastering generative AI empowers Saudi talent to lead in the region’s AI-driven future.
Your municipality’s residents will learn how to break through these barriers and start landing interviews
Call us to learn more about this AI course
Why Saudi AI Enthusiasts Should Participate in This Workshop
- AI is now a national priority. Saudi Arabia’s Vision 2030 mandates rapid adoption of AI across sectors—this workshop helps you align your skills with national transformation goals.
- Most talk AI. Few apply it. This workshop bridges the gap between theory and execution—equipping you with hands-on experience in generative AI tools and use cases.
- Enterprise-ready training. Learn how to implement LLMs, diffusion models, automation workflows, and AI-assisted development within real business and government contexts.
- Not just tools—strategic thinking. Understand how to lead AI transformation initiatives, make build-vs-buy decisions, and deploy AI responsibly and at scale.
- Designed for leadership and impact. Whether you’re building products, modernizing operations, or advising clients, this course gives you frameworks to drive measurable outcomes.
- Competitive edge in a fast-moving market. In Saudi’s AI-driven economy, those who can translate AI into impact will lead—this is your opportunity to be one of them.
Overview of This Course
Purpose : Equip professionals with hands-on expertise in generative AI for solving real-world business challenges across industries.
- Format : 5-day intensive, instructor-led workshop combining foundational theory, practical labs, and business case discussions.
Key Focus Areas:
- Neural networks, autoencoders, GANs, and diffusion models
- Large Language Models (LLMs) and Transformer architecture
- Prompt engineering, fine-tuning (including RLHF and LoRA)
- Multimodal AI: vision-language models, text-to-image/video generation
- AI-assisted programming and advanced code generation
- Business process automation and AI workflow integration
- Deployment strategies, governance, and responsible AI practices
Outcomes: - Build working knowledge of generative AI tools and architectures
- Design, implement, and deploy AI workflows in enterprise settings
- Understand ethical, technical, and strategic implications of AI
- Apply skills in a comprehensive capstone project
Who It’s For: - AI enthusiasts, data professionals, technical managers
- Product leads, digital transformation teams, and decision-makers
- Government, corporate, and academic stakeholders seeking to lead in AI adoption.
How Will Participants Benefit?
Gain practical AI expertise, not just theory.
Learn by doing—through hands-on exercises, real-world tools, and guided labs across all major generative AI models.
Bridge the gap between AI and business value.
Understand how to turn generative AI capabilities into deployable solutions that solve actual business problems.
Master cutting-edge tools and techniques.
Get direct experience with LLMs, diffusion models, vision-language systems, RAG pipelines, and AI coding assistants.
Develop strategic thinking for AI leadership.
Learn how to evaluate build-vs-buy, drive AI adoption in teams, and communicate AI value to stakeholders.
Accelerate your role in digital transformation.
Be ready to lead or contribute to AI-driven projects within your organization or sector.
Build a deployable AI use case.
Apply your skills in a capstone project that simulates enterprise-level AI solution design, from model selection to ethics and deployment.
Join a future-focused peer network.
Collaborate with fellow professionals from Saudi Arabia’s growing AI and innovation ecosystem.
Day 1 – ML Foundations & Core Generative Architectures
Session 1: Neural Networks for Generation
Description:
Introduces the fundamentals of neural networks and their role in generative AI.
Exercises:
Conceptual walkthrough of a basic neural network
Experiment with different activation functions
Learning Outcomes:
Understand neural network components (neurons, layers, activation functions)
Grasp how networks learn patterns from data
Differentiate network types used in generative tasks
Session 2: Autoencoders & VAEs
Description:
Covers Autoencoders and Variational Autoencoders for data compression and generation.
Exercises:
Visualize latent space interpolation in a VAE
Discuss VAE applications in business scenarios (e.g., anomaly detection)
Learning Outcomes:
Understand encoder-decoder architectures
Learn latent space representation and reconstruction
Apply VAEs to practical use cases like anomaly detection or data augmentation
Session 3: Generative Adversarial Networks (GANs)
Description:
Explores adversarial learning via GANs and their applications in media, design, and security.
Exercises:
Analyze case studies of GANs in real-world applications
Debate ethical implications of synthetic content generation
Learning Outcomes:
Identify components of GANs: Generator and Discriminator
Understand adversarial training and loss functions
Explore conditional GANs and industry use cases (e.g., fraud detection, realistic media synthesis)
Session 4: Introduction to Diffusion Models
Description:
Introduces diffusion models as a leading technique for high-quality generative outputs.
Exercises:
Simulate a forward and reverse diffusion process
Compare outputs from GANs, VAEs, and Diffusion models
Learning Outcomes:
Grasp the forward (noise) and reverse (denoising) diffusion process
Understand U-Net architecture and score-based modeling
Identify use cases for conditional diffusion (e.g., text-to-image generation)
Day 2 – Large Language Models & Transformer Architecture
Session 1: Transformer Architecture Essentials
Description:
Demystifies the architecture powering modern LLMs like GPT and BERT.
Exercises:
Trace the data flow through a simplified Transformer block
Learning Outcomes:
Understand self-attention and positional encoding
Compare encoder-decoder and decoder-only Transformer models
Learn how parallelization improves training efficiency
Session 2: Language Model Training Process
Description:
Explains how LLMs are trained, fine-tuned, and guided through prompt engineering.
Exercises:
Write prompts for tasks like summarizing meetings, drafting emails, or generating descriptions
Learning Outcomes:
Understand pretraining objectives (Masked LM, next-token prediction)
Learn fine-tuning techniques including SFT and transfer learning
Master prompt engineering (zero-shot, few-shot, chain-of-thought)
Session 3: Advanced Training Techniques (RLHF, LoRA)
Description:
Covers alignment and efficiency techniques to improve model performance.
Exercises:
Compare outputs before and after RLHF tuning
Discuss trade-offs of LoRA vs full fine-tuning
Learning Outcomes:
Understand the RLHF pipeline (SFT, reward modeling, PPO)
Learn LoRA for parameter-efficient fine-tuning (PEFT)
Identify optimal use cases for each approach
Session 4: Advanced Text Generation Techniques
Description:
Teaches how to control and enhance the quality of LLM-generated text.
Exercises:
Experiment with temperature, top-k, and top-p settings on sample prompts
Learning Outcomes:
Explore decoding strategies (greedy, beam, sampling)
Use controls for tone, creativity, and factuality
Apply Retrieval-Augmented Generation (RAG) to improve reliability and reduce hallucinations
Day 3 – Multimodal AI & Vision-Language Models
Session 1: Computer Vision for Generation
Description:
Introduces how generative AI models understand and produce visual data using deep learning techniques.
Exercises:
Analyze an image dataset to understand feature extraction
Explore how CNNs support image-based generation tasks
Learning Outcomes:
Understand image representation (pixels, channels)
Learn the role of CNNs in extracting visual features
Discover foundational techniques like image-to-image translation (Pix2Pix, CycleGAN)
Session 2: Vision-Language Model Architecture
Description:
Explains how models like CLIP combine vision and language for tasks such as captioning and visual question answering.
Exercises:
Generate descriptive captions for sample images using a VLM demo
Discuss cross-modal use cases
Learning Outcomes:
Understand embeddings for both images and text
Learn contrastive learning and cross-attention techniques
Explore VLM applications in accessibility, e-commerce, and customer service
Session 3: Advanced Text-to-Image Generation
Description:
Covers advanced methods in text-to-image diffusion models with fine-grained control using tools like ControlNet.
Exercises:
Generate images using varied prompts and control parameters (e.g., style, structure, negative prompts)
Learning Outcomes:
Use classifier-free guidance and prompt conditioning
Understand inpainting, outpainting, and image-to-image techniques
Apply tools like ControlNet for precise output generation
Session 4: Video & 3D Generation
Description:
Explores the emerging field of AI-generated video and 3D content, with applications in entertainment and design.
Exercises:
Review demos of AI-generated video clips and 3D assets
Analyze the challenges of motion control and rendering
Learning Outcomes:
Learn about latent video diffusion and frame-by-frame generation
Understand Neural Radiance Fields (NeRFs) and 3D mesh creation
Explore generative AI for VR/AR, simulation, and digital prototyping
Day 4 – Code Generation & Business Applications
Session 1: AI-Assisted Programming
Description:
Demonstrates how AI tools like GitHub Copilot can support developers in writing, debugging, and documenting code.
Exercises:
Generate code from natural language prompts
Debug existing code with AI assistance
Learning Outcomes:
Understand AI coding assistant workflows
Apply AI to code generation, explanation, and refactoring
Improve developer productivity with practical AI integration
Session 2: Advanced Code Generation Techniques
Description:
Explores complex code generation, AI-assisted testing, and best practices for code quality and security.
Exercises:
Generate unit tests for a given function
Discuss AI-aided security checks and TDD workflows
Learning Outcomes:
Fine-tune LLMs on domain-specific codebases
Apply AI for automated reviews and linting
Evaluate AI-generated code for correctness and maintainability
Session 3: Business Process Automation
Description:
Applies generative AI to automate core functions in marketing, operations, and support.
Exercises:
Design a content generation workflow for marketing or sales
Automate data extraction and summarization
Learning Outcomes:
Identify automation opportunities across departments
Use AI to create dynamic, personalized content at scale
Implement intelligent document and communication workflows
Session 4: Integration & Workflow Design
Description:
Teaches how to connect generative AI models into production systems and scalable architectures.
Exercises:
Outline a system that integrates a text-to-image model into an e-commerce platform
Learning Outcomes:
Build workflows with REST APIs, LangChain, and microservices
Design pipelines for data input/output
Monitor and orchestrate end-to-end AI systems
Day 5 – Advanced Applications & Production Deployment
Session 1: Custom Model Development
Description:
Covers the lifecycle of developing, training, and evaluating a custom generative model.
Exercises:
Propose a business use case for custom model development
Define dataset requirements and success metrics
Learning Outcomes:
Understand when to build vs buy
Learn dataset curation, transfer learning, and hyperparameter tuning
Evaluate generative models with metrics like FID, BLEU, and ROUGE
Session 2: Production Deployment Strategies
Description:
Teaches how to deploy generative models reliably using modern DevOps and MLOps best practices.
Exercises:
Design a full deployment pipeline from training to inference serving
Learning Outcomes:
Understand deployment with FastAPI, Docker, and Kubernetes
Use cloud platforms like AWS SageMaker, Azure ML, GCP Vertex AI
Monitor performance, drift, bias, and maintain model versions
Session 3: Safety, Ethics & Governance
Description:
Addresses the ethical, legal, and governance challenges of deploying generative AI in sensitive or regulated environments.
Exercises:
Analyze an AI system for risks and propose mitigation strategies
Learning Outcomes:
Identify bias, hallucinations, privacy, and copyright concerns
Learn XAI (Explainable AI) and responsible AI frameworks
Develop internal AI governance policies
Session 4: Future Trends & Capstone Project
Description:
Concludes the course with a group-based capstone project and a look ahead at where generative AI is heading.
Exercises:
Design a generative AI solution for a real business problem
Present scope, data, tools, ethics, and deployment strategy
Learning Outcomes:
Apply all course learnings to a full-cycle use case
Explore cutting-edge trends: foundation models, AI agents, green AI
Strategize for human-AI collaboration and future integration
Call us to learn more about this AI course
FAQs
1. What level of AI knowledge is required to join this course?
Answer: The course is designed for professionals with basic familiarity with AI or machine learning concepts. No advanced coding experience is required, but a working understanding of data and digital systems will help participants fully engage in the hands-on exercises.
2. Will I learn how to build Large Language Models (LLMs) from scratch?
Answer: No. The course focuses on using and fine-tuning pre-trained LLMs (like GPT) rather than building them from scratch. You will learn prompt engineering, supervised fine-tuning (SFT), and parameter-efficient techniques like LoRA.
3. Does the course include hands-on practice?
Answer: Yes. Every session includes practical exercises such as generating text, testing decoding strategies, building prompt templates, automating business workflows, and designing deployment pipelines. The course is highly interactive.
4. What kind of generative AI models will be covered?
Answer: The course covers a wide range of generative models:
Neural Networks
Autoencoders and Variational Autoencoders (VAEs)
Generative Adversarial Networks (GANs)
Diffusion Models
Large Language Models (LLMs)
Vision-Language Models (VLMs)
Text-to-Image, Text-to-Video, and 3D Generation systems
5. Will I get to work on real-world business problems?
Answer: Yes. The capstone project challenges participants to design a generative AI solution to a real business use case—like automating product design, creating personalized content, or deploying AI-enhanced customer support.
6. How does the course cover the ethical use of AI?
Answer: Day 5 includes a full session on safety, ethics, and governance. You’ll learn about bias, hallucinations, misinformation risks, and strategies for mitigation. It also covers AI governance frameworks and responsible deployment practices.
7. What tools and platforms will I use during the course?
Answer: You will conceptually engage with tools like:
GitHub Copilot & AI coding assistants
Text-to-image and text-to-video generation platforms
Workflow orchestration tools (e.g., LangChain)
Cloud deployment platforms (e.g., AWS SageMaker, Azure ML, GCP Vertex AI)
The course emphasizes understanding how these tools integrate into business systems.
8. Will I learn how to integrate AI into existing enterprise systems?
Answer: Yes. Day 4 includes a session on integration and workflow design, teaching you how to build scalable architectures with REST APIs, microservices, and orchestration frameworks. Monitoring and logging best practices are also covered.
9. How does this course differ from other AI courses online?
Answer: Unlike typical online courses focused only on theory or code, this program blends strategic thinking with applied exercises. It’s built to help professionals in Saudi Arabia directly apply generative AI to enterprise, government, and entrepreneurial initiatives.
10. Is there a certificate or proof of completion?
Answer: While the document doesn’t specify a certificate, this course is designed to align with professional upskilling goals. Participants who complete the final capstone project and attend all sessions will receive recognition of completion (subject to organizing institution).