Certified Generative AI Architect with Knowledge Graphs

Explore how graph databases model entities and relationships to support knowledge graphs, AI reasoning, and flexible traversals with nodes, edges, properties, labels, and RDF graphs and property graphs.

Discover how the semantic web enables machines to link concepts, infer relationships, and reason across sources using RDF triples, OWL, and SPARQL to power knowledge graphs.

Explore ontology design with Protégé and TopBraid Composer, creating classes, properties, and axioms to give AI systems semantic context, validation, and interoperable data governance.

Explore how Sparql, Cypher, and Gremlin drive graph querying across RDF graphs, ontologies, and linked data, from pattern matching to deep traversals.

Learn how ontology lifecycle management and governance keep ontologies living, credible, and aligned with enterprise needs. Design, validate, deploy, and evolve ontologies with versioning, SHACL rules, and governance processes.

Transform data into RDF triples for a semantic knowledge graph by mapping structured and unstructured sources (CSV, JSON, PDFs) via ETL, and apply NLP to extract entities and relationships.

Build scalable ingestion pipelines (ETL) that extract data from diverse sources, transform it to RDF or Json-ld, and load it into a graph database.

Master entity linking and disambiguation in knowledge graphs by consolidating duplicates across intra- and cross-graph sources, using canonical URIs and contextual disambiguation across metadata and external sources with human-in-the-loop validation.

Learn to ground LLMs with knowledge graphs, reducing hallucinations through structured context, disambiguation, and real-time facts, using prompt injection, hybrid retrieval, and graph queries via SPARQL or Cypher.

Explore rag pipelines—five parts: query parser, retriever, reranker, prompt assembler, llm generator—and how vector, graph, and hybrid retrieval with metadata filters deliver up-to-date, grounded answers.

Leverage vector databases like FAISS, Pinecone, and Weaviate to store embeddings, run semantic vector searches, and retrieve relevant chunks with metadata filtering in a Rag pipeline.

Leverage a hybrid Rag pipeline that uses graph queries and vector ranking in parallel, merged by a combiner, to deliver accurate, explainable, contextually grounded results.

Learn how semantic ranking and query rewriting in a Rag pipeline interpret user intent, improve retrieval, and ensure the final prompt is accurate and complete.

Evaluate rag systems by measuring retrieval and generation with precision at k, recall at k, factual accuracy, and language quality; monitor token usage, hallucinations, and cost using a hybrid approach.

Orchestrate multiple AI agents to work together toward a shared goal, handling retrieval, planning, and summarization. Compare Landgraf, Autogen, and crew AI for modular workflows with memory and role-based pipelines.

Explore memory types (short-term, long-term, episodic), tools, and planning in agentic architectures to retain context, access APIs, and orchestrate multi-step tasks using chain-of-thought reasoning.

Multi-agent collaboration unlocks higher order problem solving through parallel tasks and domain specialization, using planners, retrievers, graph reasoners, and synthesizers to boost speed, accuracy, and scalability in complex workflows.

Design domain-specific agents with four ingredients: role, data, tools, and reasoning strategy; leverage knowledge graphs, episodic memory, and rag pipelines for real-time domain reasoning.

Explore how AWS, Azure, and GCP offer scalable, managed AI infrastructure. Leverage tools like SageMaker, Bedrock, Vertex AI, and GKE for faster AI deployment.

Containerize ai APIs with FastAPI, packaging model, inference logic, prompts, and security hooks; deploy and scale via Kubernetes with horizontal and vertical scaling, container registry, ingress, and observability for production.

Explore RESTful gen AI delivery with authentication, logging, and observability across endpoints like Slack and summarize. Compare serverless options on AWS Fargate and Azure Container Apps for scalable, on-demand inference.

Implement secure deployment patterns that protect Gen AI apps with secure APIs, protected secrets, input sanitization to block prompt injection, and automatic recovery via API gateways and RBAC secret management.

Monitor gen ai apps with system metrics, token usage, prompt failures, and API error codes, while logging and tracing provide observability to debug issues and optimize prompts.

Merge ontologies with prompts to ground llms in structured, factual medical knowledge graphs. Use sparql queries, entity recognition, and concept trees to boost domain accuracy and explainability.

Link LLMs with knowledge graphs to disambiguate concepts and enable reasoning with facts, not guesses, injecting resolved concepts into prompts.

Design patterns provide reusable blueprints to build ontology-driven gen AI systems that retrieve facts via sparql into slot-based prompts, with semantic middleware and feedback loops.

Domain aware and context aware Gen AI drives trust and reliability in healthcare, law, and enterprise by using ontologies, patient profiles, and role-based access to deliver relevant, compliant responses.

Expose your gen AI graph intelligence as rest apis to unify enterprise systems through api gateways. Governance of data with ontology-driven validation and ETL pipelines feeding a knowledge graph.

Translate business objectives into gen AI architectures by mapping goals to AI capabilities using a knowledge graph and a mapping table, then design outcomes-driven solutions with justified technologies.

Lead cross-functional AI and data teams by orchestrating architects, engineers, product, and cloud roles through sprint planning and decisions that align latency, precision, cost, and features.

Collaborate with cloud architects and MLOps teams to design scalable compute, robust APIs, and guardrails for cost-efficient, observable gen AI systems. Operationalize prompts, monitor performance, and deploy reliably across environments.

Lead with the problem and translate architecture into executive value through visuals and concise one-pagers, quantifying time savings, cost reductions, and risk reductions.

Leverage reusable templates, blueprints, and playbooks as a certified generative AI architect with knowledge graphs to scale Gen AI pipelines and maintain a living repository as your system of record.

Create living visual architecture diagrams that map the LM layer and data flows through agents, tools, and retrieval systems, clarifying APIs, storage, and orchestration.

Create ontology design docs that give purpose, provenance, and persistence to every class, relation, and property, enabling traceability and clear reasoning for AI engineers, domain experts, and knowledge stewards.

Translate a generative AI architecture into audience-specific narratives for CTOs, product owners, data scientists, and finance, highlighting cost, risk, and why tuning versus prompting matters.

Navigate generative AI projects with JIRA or Azure DevOps by mapping work to backlog items, organizing epics and tasks with acceptance criteria, ownership, definitions of done, and sprint reviews.

Sprint planning brings focus and measurable goals to ambiguous work, while milestones shown on dashboards track accuracy, latency, and better agent decisions in Gen AI.

Start with the problem, not the model, and use knowledge driven ai and graph powered ai to improve structured retrieval, disambiguation, and traceability for business outcomes.

Explore building an ontology as the backbone of graph-enabled RAG pipelines, exporting to RDF or OWL, and integrating semantic graph reasoning with vector search for traceable, grounded LLM prompts.

Deploy a modular, multi-agent gen AI pipeline to the cloud with environment-based configuration, strong logging, and observability to ensure secure, scalable, and reliable operations.

Document architecture, reasoning, and deployment for generative ai systems to make decisions transparent and auditable, building trust through clear, scalable documentation that acts as a product.

Present to a panel of experts by articulating the business challenge, architectural decisions, data-to-decisions storytelling, trade-offs, and how the graph and logs demonstrate a scalable value.