Deep dives into AI, data architecture, knowledge graphs, and enterprise platform engineering.
9 articles
An in-depth comparison of GraphQL and REST architectures using a Spring Boot implementation, exploring query flexibility, performance characteristics, over-fetching solutions, and real-world use cases for each approach.
An in-depth exploration of how Large Language Models are transforming knowledge engineering, from ontology construction and semantic reasoning to production-grade RAG systems and LLMOps best practices.
A comprehensive guide to implementing the complete deep learning lifecycle on AWS SageMaker, covering data preparation, CNN model training with Keras/TensorFlow, multi-GPU scaling, and production deployment with TensorFlow Serving.
A deep dive into TaxonomyLLM, a novel transformer architecture that leverages disentangled TopoAttention to automatically translate SQL schemas into valid RDF taxonomies, bridging the gap between relational data and semantic knowledge graphs.
Deep dive into building a production-grade Retrieval-Augmented Generation (RAG) system that combines LLMs, vector similarity search with Annoy, and advanced query intelligence for retail analytics.
A rigorous exploration of the mathematical foundations powering modern vector search systems, including distance metrics, similarity functions, and ANN algorithms that enable semantic search at scale.
A comprehensive guide to implementing Enterprise Integration Patterns (EIP) using Apache Camel with AWS services including S3, Kinesis, and Apache Kafka for building robust data engineering pipelines.
A comprehensive guide to implementing production-ready conversational AI assistants using LangChain4j, semantic embeddings, and Spring Boot for enterprise customer service applications.
Build an intelligent email summarization system using Spring WebFlux, Gmail API with OAuth 2.0, and OpenAI integration for extracting actionable insights from your inbox.