Design and operate a low-latency AI anomaly detection API for streaming data—architecture, models, thresholds, evaluation, and operations.
Design, build, and scale an API-driven data labeling pipeline with quality gates, active learning, and strong governance.
Build a practical GraphRAG pipeline: extract a knowledge graph, index nodes and chunks, retrieve local paths and global summaries, and synthesize grounded answers.
A step-by-step guide to preparing high-quality datasets for LLM fine-tuning, from sourcing and cleaning to formats, safety, splits, and evaluation.
Build a production-ready tutorial for knowledge graph–enhanced AI retrieval: schema, ingestion, Cypher, hybrid search, and evaluation.
