Elevate Your Skills as a Generative AI Architect

The Certified Generative AI Architect with Knowledge Graphs program offers an in-depth, advanced-level certification tailored for professionals eager to master cutting-edge Generative AI (GenAI) systems. This comprehensive course is designed to give learners the tools, frameworks, and hands-on experience necessary to architect intelligent, explainable, and scalable AI solutions. By diving into the synergy of Large Language Models (LLMs), Knowledge Graphs, and Retrieval-Augmented Generation (RAG), you’ll learn to create production-grade applications that extend beyond simple prompt engineering.

Starting with the foundations of GenAI architecture, you’ll explore the capabilities of modern LLMs and the advent of agentic AI systems. Delve into the essential components of context, memory, and reasoning and their role in optimizing AI performance. You’ll gain insights into RAG pipelines while setting the groundwork for knowledge-enhanced AI applications. The course further covers the design and construction of ontologies using tools like Protégé and TopBraid Composer, as well as querying graph databases through RDF, OWL, SPARQL, and Cypher, empowering you to manage enterprise-level knowledge systems effectively.

As the course progresses, advanced topics such as hybrid retrieval systems will be tackled, integrating vector databases like FAISS, Weaviate, and Pinecone with graph-based reasoning to enhance contextual relevance and minimize LLM hallucinations. You’ll orchestrate multi-agent GenAI systems using frameworks such as LangGraph and AutoGen to create collaborative, modular workflows. Strong emphasis is placed on cloud-native deployment strategies, ensuring your GenAI systems are scalable and enterprise-ready. Cap your learning experience with a comprehensive capstone project that includes defining a business problem, building a knowledge graph-enabled RAG pipeline, and demonstrating your solution with executive-ready documentation.

What you will learn:

• Design end-to-end Generative AI architectures that combine LLMs, retrieval-augmented generation (RAG), agent workflows, and knowledge graphs.
• Model and implement ontologies and semantic knowledge graphs using tools like Protégé, RDF/OWL standards, and graph databases such as Neo4j or Stardog.
• Build hybrid retrieval systems that integrate vector search (FAISS, Pinecone, Weaviate) with graph-based semantic querying for enhanced context and relevance.
• Develop multi-agent GenAI applications using LangGraph, AutoGen, or CrewAI, enabling memory-aware, tool-using, and role-based intelligent agents.
• Deploy and scale GenAI systems in cloud-native environments using Docker, Kubernetes, AWS Fargate, and Azure Container Apps with observability and monitoring.
• Translate business problems into knowledge-driven AI solutions and deliver stakeholder-ready architecture, documentation, and ROI narratives.

Course Content:

• Sections: 10
• Lectures: 61
• Duration: 2h 3m

Requirements:

• Basic understanding of AI/ML concepts (e.g., what LLMs, embeddings, and APIs are).
• Familiarity with Python programming (intermediate level preferred for building pipelines and agent workflows).
• Experience with cloud platforms such as 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 (no prior mastery required).
• A laptop or workstation with an internet connection and access to tools.
• No formal degree or prior knowledge of knowledge graphs or agents is required—everything will be explained step by step through interactive labs, visuals, and walkthroughs.

Who is it for?

• 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 using modern deployment patterns and cloud-native tooling.
• Data Engineers and Knowledge Graph Practitioners who are expanding into Generative AI and want to leverage RDF, OWL, SPARQL, and graph models in AI workflows.
• Technical Product Managers and Tech Leads who need to understand how to structure multi-agent systems, integrate LLMs with enterprise data, and align technical architectures with business goals.
• Semantic Web or Ontology Engineers aiming to apply their expertise in the fast-evolving world of LLMs, agentic workflows, and context-driven GenAI applications.

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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