Certified Generative AI Professional

The Certified Generative AI Professional (CGAIP) is a project-driven program designed to equip participants with the advanced skills, tools, and real-world experience needed to excel in the fast-evolving field of Generative AI. Covering the full spectrum—from LLM architecture and advanced prompting to LangChain workflows, Ollama deployments, and multi-agent systems—this certification prepares learners to build and deploy production-grade AI applications. Through advanced hands-on labs and expert-led sessions, learners build real-world Gen AI applications, and the program concludes with a Capstone Project that showcases the ability to design and deploy production-ready AI systems using cutting-edge tools. Graduates are ready for high-impact roles such as Generative AI Engineer, LLM Workflow Architect, AI Application Developer, and Prompt Engineering Specialist.

To ensure participants are fully prepared for the advanced content of this course, the following certifications are mandatory: Certified Data Scientist Professional (CDSP) — covering the full data science pipeline including Python, SQL, ML, and deployment; and the Deep Learning Masterclass — equipping you with essential knowledge in neural networks, CNNs, RNNs, and foundational NLP. These prerequisites ensure you have the technical depth needed to succeed in building and deploying advanced Generative AI systems.

How you'll learn

• Instructor-Led Training (ILT): Participate in interactive, classroom-based sessions led by experienced industry professionals for an immersive learning experience.
• Virtual Instructor-Led Training (VILT): Join live online classes from anywhere, offering flexibility without compromising interactivity and mentorship.

• DL, AI & ML EngineersEngineers aiming to build real-world LLM applications.
• Data ScientistsData scientists ready to go beyond traditional models.
• Tech Leads & ArchitectsTech leads and architects looking to integrate LangChain, Ollama, and multi-agent systems into scalable AI solutions.

• Build and deploy advanced LLM-based applications using LangChain and Ollama.
• Design and optimize prompt workflows, multi-agent systems, and RAG pipelines.
• Fine-tune local models and implement real-world Gen AI projects.
• Deploy interactive apps with Gradio and manage scalable AI systems.
• Showcase their Generative AI expertise through a portfolio-ready Capstone Project.

Program Hours

76 Hours.

Corporate Training

10 full days, with each day lasting 7–8 hours.

Individual Learners

A 2.5-month program with 2 sessions per week, each lasting 4 hours; or a 3-month program with 1 session per week, each lasting 6 hours.

Locations

USA (Newark, Delaware); Saudi Arabia (Al-Hassan Bin Aly, Riyadh); UAE (Jumeirah Lake Towers, Dubai — partnership with Elbadya Quantum); Egypt (New Cairo; Nasr City, Cairo; Mohandessin, Giza).

Upon successful completion of the Certified Generative AI Professional (CGAIP) program, participants will be awarded an official certificate of completion issued by EPSILON AI – Delaware, USA, recognizing their advanced expertise in generative AI, large language models, and end-to-end AI system deployment. The program is delivered in alignment with international standards, including ISO 21001 and ISO 9001, and follows globally recognized frameworks such as IACET and CPD, ensuring a high-quality, industry-relevant learning experience. All graduates receive a digitally authenticated certificate containing a unique Certificate ID and personal EPSILON ID, enabling instant, secure online verification—ideal for professional portfolios, LinkedIn profiles, and employer validation. In addition, a prestigious hardcopy certificate is issued, printed with advanced security and branding features including a unique serial number and verification code, a gold-embossed seal with authorized EPSILON AI signatures, and UV-printed security elements for enhanced authenticity and protection against forgery.

• Attend a minimum of 80% of live instructional sessions (direct contact hours).
• Achieve a minimum score of 80% on the final examination.
• Successfully complete and present the Capstone Project.

• Gen AI Landscape & Transformer Architecture: Gen AI evolution from RNNs to Transformers and an overview of model families (GPT, BERT, T5, etc.); Transformer deep dive—multi-head attention, positional encoding, scalability. Hands-on: exploring a public notebook of a Transformer from scratch, and a small experiment generating text with a basic Hugging Face model.
• LLM Internals & Model Training: Pre-training vs. fine-tuning—datasets, tokenization, training objectives; scaling laws—how performance scales with data and parameters. Hands-on: fine-tuning a small/distilled model on a sample dataset (e.g., GPT-2 small), and analyzing logs and performance metrics (loss, perplexity).
• State-of-the-Art LLMs: Comparison of models—GPT-4, Llama, Falcon, etc.; evaluation metrics—BLEU, ROUGE, perplexity, human vs. automated assessment. Hands-on: output comparison using open-source models, and benchmarking model quality.
• Ethics & Responsible AI: Ethical issues—bias, fairness, safety; regulatory concerns—data privacy, licensing. Hands-on: prompting for bias/harmful content, and a group discussion on mitigation and responsible use.

• Fundamentals & Setup: LangChain overview—Chains, Tools, Agents, Memory; environment setup, Python practices, and GPU considerations.
• Custom Chains & Workflow Management: Chain types—sequential, branching, custom logic; memory—conversation buffer and summary-based. Hands-on: multi-step chains on custom knowledge, and memory integration examples.
• Agents, Tools & Plugins: Agents—tool selection and chain invocation; tool integration—APIs, files, web. Hands-on: an agent to switch between tools or answer types, and custom tool integration (e.g., a sentiment API).
• Testing, Debugging & Complex Use Cases: Multi-agent workflows; profiling, caching, and debugging with LangSmith. Hands-on: an advanced agent-based solution, and using tracers/logging to optimize performance.

• Ollama Overview & Deployment: Supported models, system requirements, and CLI and Python setup. Hands-on: install and deploy a Llama-style model locally, and prompt usage via CLI or Python.
• Optimization & Resource Usage: Quantization and pruning—4-bit, 8-bit. Hands-on: compare quantized vs. full-size model speed and memory.
• Customization & LangChain Integration: Fine-tuning (LoRA or PEFT); LangChain + Ollama integration. Hands-on: tune a model on domain data, and configure LangChain with Ollama for inference.
• Troubleshooting & Monitoring: Hardware/software pitfalls, environment configs; logging and performance monitoring. Hands-on: simulate production with high-load requests, and dashboard or CLI-based usage tracking.

• Ingestion, Indexing, and Retrieval: Pulling text from varied sources; embeddings and vector stores (FAISS, Pinecone). Hands-on: connect to a vector DB via LangChain, and retrieve relevant data chunks.
• RAG Architecture and Application: End-to-end RAG pipeline overview; LangChain + Ollama in a RAG setup. Hands-on: build a basic RAG using LangChain and Ollama.
• Advanced RAG Features: Chunking, hybrid search, reranking; metadata and query translation. Hands-on: implement an advanced RAG scenario.

• Advanced Agents & Task Decomposition: Agents calling agents; hierarchical task delegation. Hands-on: a multi-agent system using LangChain + Ollama.
• Capstone Project Development: Implementation planning. Hands-on: build an advanced RAG or multi-agent system.
• Gradio UI: Gradio components and interface design. Hands-on: a basic chatbot UI with Gradio.
• Gradio Vision & Deployment: Gradio for images/videos; Docker/cloud deployment strategies; documentation (README, guides, prompt libraries). Hands-on: an image similarity app with Gradio.
• Presentations & Reflection: Capstone presentation and peer feedback; trends—RLHF, multimodal, open source; wrap-up, graduation, and resources.