Elevate Your AI Skills: Become a Certified Generative AI Architect

The Certified Generative AI Architect with Knowledge Graphs program is designed to equip professionals with the advanced skills necessary to architect state-of-the-art Generative AI (GenAI) systems that are not only intelligent but also explainable and scalable. Throughout this course, learners will engage with the latest Large Language Models (LLMs) and Knowledge Graph technologies, diving into practical, hands-on experiences to build production-grade AI solutions that transcend basic prompt engineering.

Starting with foundational GenAI architecture, participants will familiarize themselves with the workings of modern LLMs and the advent of agentic AI systems. The course meticulously covers how memory, context, and reasoning capabilities can substantially augment system performance, providing insights into the structure of Retrieval-Augmented Generation (RAG) pipelines and their critical role in knowledge-enhanced AI applications. As the course progresses, students will learn to design and implement ontologies and manage knowledge systems effectively, utilizing essential tools like Protégé and graph databases such as RDF and Neo4j.

In the advanced phases of the course, attendees will be introduced to multi-agent systems and cloud deployment strategies. This includes orchestrating intelligent agents that can collaborate efficiently on various tasks while deploying scalable GenAI systems across platforms like AWS and Azure. By the end of the program, participants will not only have built a knowledge graph-enabled RAG pipeline but will also complete a capstone project that encapsulates their learning experience with real-world applications, producing documentation and architecture blueprints suitable for executive presentation. This comprehensive course is ideal for AI professionals eager to lead the next generation of intelligent AI architectures.

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.

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).

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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