01Module 09: Human-in-the-LoopMaster human-in-the-loop AI systems - annotation pipelines, active learning, feedback collection, escalation patterns, and measuring HITL effectiveness.02Why Human-in-the-Loop MattersUnderstand why full automation fails, where the alignment gap lives, what regulations demand, and how to design the right level of human oversight for any AI system.03Annotation PipelinesData labeling workflows, annotation guidelines, inter-annotator agreement, conflict resolution, and quality control for training data that powers AI systems.04Active LearningSelecting the most informative samples for labeling - uncertainty sampling, diversity strategies, query-by-committee, and LLM-based active learning for text classification.05Human Feedback CollectionCollecting preference data, thumbs ratings, and corrections for RLHF pipelines - preference interface design, feedback quality controls, DPO data formats, and ELO-based model ranking.06Escalation and Handoff PatternsDesigning AI systems that know when to stop and hand off to humans - confidence thresholds, sentiment detection, topic-based routing, context transfer, and escalation orchestration.07Review Queues and ToolingBuilding production review interfaces, priority queues, audit trails, reviewer dashboards, and HITL tooling - from Redis-backed queue management to Label Studio integration.08Measuring HITL EffectivenessEnd-to-end metrics for human-in-the-loop systems - false positive/negative rates, confidence calibration, inter-rater reliability, reviewer performance tracking, ROI computation, and system-level effectiveness dashboards.
