Most Generative AI courses teach you prompt engineering. This one teaches you to architect production grade GenAI systems that combine Large Language Models, Knowledge Graphs, Retrieval Augmented Generation (RAG), and multi agent orchestration. You will learn to design ontologies using Protégé, build graph databases with Neo4j and RDF/OWL, integrate vector search (FAISS, Pinecone, Weaviate) with graph based reasoning, and develop multi agent applications using LangGraph, AutoGen, or CrewAI. You will deploy to cloud native environments with Docker, Kubernetes, AWS Fargate, and Azure Container Apps.

This Course Offers

• A complete architecture for knowledge aware GenAI systems that are explainable, scalable, and reduce hallucinations.
• Hands on ontology engineering using Protégé and TopBraid Composer, plus graph querying with SPARQL and Cypher.
• Hybrid retrieval systems combining vector similarity with semantic filtering and graph traversal.
• Multi agent orchestration for planning, retrieval, reasoning, and summarization tasks.
• A capstone project where you define a business problem, build a knowledge graph enabled RAG pipeline, deploy a multi agent app to the cloud, and present architecture blueprints.

Why We Love This Course

1. It bridges the gap between semantic web technologies and modern GenAI. Knowledge graphs are the missing piece for enterprise grade AI, and this course teaches that integration.
2. Over 17,000 students have enrolled, and the course is structured as a certification path for AI architects.
3. The instructor covers real world deployment patterns (Docker, Kubernetes, serverless) and enterprise concerns like monitoring, observability, and secure rollout.
4. The course includes labs and walkthroughs, with no prior mastery of knowledge graphs or agents required.

The course last updated in February 2026. If you are an AI or ML engineer, solution architect, data engineer, or technical product manager who wants to move beyond prototyping chatbots to building explainable, enterprise ready GenAI systems, this course gives you the architecture, tools, and vocabulary to lead.

• AI/ML engineers looking to deepen their understanding of LLMs, RAG pipelines, and knowledge aware AI applications.
• Solution and cloud architects who want to design scalable, secure, and context aware GenAI systems.
• Data engineers and knowledge graph practitioners expanding into Generative AI.
• Technical product managers and tech leads who need to understand multi agent systems and align technical architectures with business goals.
• Semantic web or ontology engineers aiming to apply their expertise in the world of LLMs and agentic workflows.
• Professionals building AI for healthcare, legal tech, finance, or retail who need explainable, knowledge aware systems.

• Basic understanding of AI/ML concepts (LLMs, embeddings, APIs) is required.
• Familiarity with Python programming (intermediate level) is required for building pipelines and agent workflows.
• Experience with cloud platforms (AWS, Azure, or GCP) basic knowledge of compute, storage, and containers is helpful.
• Interest or experience in semantic technologies like RDF, OWL, or graph databases is helpful but not required.
• A laptop with internet access.
• No formal degree or prior mastery of knowledge graphs or agents is required.

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