AI
Advanced
Advanced
course description
This course offers a comprehensive, end-to-end introduction to building production-ready Generative AI applications and deploying them in cloud-native environments. Learners progress from core natural language processing concepts to advanced topics, including large language models using Azure AI Services and real-time AI systems. The course emphasizes hands-on implementation, system design, and deployment best practices, leveraging tools such as FastAPI, Docker, and Azure services to build scalable, reliable AI-powered applications.
course outcomes
Architect GenAI systems
Design multi-agent workflows
Build RAG pipelines
Develop cloud-ready APIs
Deploy Dockerized AI apps
Create multimodal AI solutions
Curriculum
Module 1: NLP & Deep Learning Foundations
Introduces natural language processing fundamentals, classical deep learning architectures, and transformer models through hands-on learning and a practical project.
Module 2: Large Language Models & Tooling
Covers core LLM concepts and modern tooling using LangChain and LangSmith, enabling learners to build and evaluate LLM-based workflows.
Module 3: Agent & Workflow Orchestration
Explores LangGraph for building structured, multi-step AI workflows through a focused applied project.
Module 4: Retrieval-Augmented Generation (RAG)
Examines RAG concepts, including vector-based and graph-based approaches, to build intelligent, knowledge-grounded AI systems.
Module 5: Cloud & Azure Fundamentals
Introduces cloud computing concepts and Azure core services, forming the foundation for cloud-native AI deployment.
Module 6: Azure AI Services
Covers Azure OpenAI, Azure AI Search, OCR, document intelligence, and speech services through applied, project-based learning.
Module 7: Deployment & API Development
Focuses on containerization with Docker and Docker Compose, API development using FastAPI, and deploying production-ready AI applications.
Final Project
Learners design, build, and present a complete end-to-end GenAI application, integrating AI models, cloud services, APIs, and deployment best practices.
Instructor
Omar SaadEldin
AI Engineer
AI engineer with a B.Sc. in Computer Science and Artificial Intelligence from Cairo University, currently working at Global Brands Group. He has strong experience in artificial intelligence, deep learning, natural language processing, computer vision, and LLM-based systems. He achieved a top-three ranking in the Amazon DeepRacer competition using optimized deep learning models. His work focuses on practical, industry-oriented AI solutions and delivering end-to-end AI projects. If you want it shorter, more technical, or more marketing-focused, I can tighten it further.
Hagar Hassan
AI Engineer
AI Engineer with a strong foundation in computer science and artificial intelligence, and hands-on experience designing, building, and deploying modern AI systems. Experienced in machine learning, deep learning, natural language processing, and computer vision. Actively involved in developing practical AI solutions and translating complex technical topics into clear, structured learning materials. Skilled in modern AI tools, cloud-based AI services, and best practices for building scalable, production-ready AI applications.