Name: Certified Generative AI Architect with Knowledge Graphs
Availability: InStock

What You'll 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.

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.

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.

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6 Courses

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