Human-in-the-Loop (HITL) QA & Data Quality Management
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Module 1 – HITL Foundations & Quality Metrics
Module 1 – Overview
Preview
What is Human-in-the-Loop AI?
Preview
The role of Humans in AI Development
Preview
Data Quality Dimensions
Preview
Cost of Poor Quality in AI Projects
Preview
Module 1 – Knowledge Check Quizzes
Preview
6 lessons, 1 quiz
Module 2 – Quality Control (QC) Techniques
Module 2 – Overview
First-Pass vs Second-Pass QA
Reviewing Annotations vs Correcting Them
Identifying Errors (missed objects, wrong labels, etc.)
Using Benchmark (Gold Standard) Data
Hands-on Lab: Spot errors in image annotation
Module 2 – Knowledge Check Quizzes
7 lessons, 1 quiz
Module 3 – Quality Assurance (QA) Pipelines
Module 3 – Overview
Multi-Layer QA Workflows
How Enterprise Annotation Pipelines Function
Audit Sampling and Error Rate Analysis
Managing Ambiguous Cases
Creating Annotation Guidelines
Hands-on Lab: Build a QA checklist / Review a dataset and produce an audit report
Module 3 – Knowledge Check Quizzes
8 lessons, 1 quiz
Module 4 – Error Categorization & Analytics
Module 4 – Overview
Types of Annotation Errors
Statistical Tracking (precision, recall, F1-score)
Annotator Performance Scoring
Quality Control (QC) Dashboards
Root Cause Analysis
Module 4 – Knowledge Check Quizzes
7 lessons, 1 quiz
Module 5 – HITL for Different Modalities
Module 5 – Overview
HITL for Vision Data (CV)
HITL for NLP Data
HITL for Audio/Speech Data
HITL for Geospatial Datasets
HITL for LLM Evaluation
Module 5 – Knowledge Check Quizzes
7 lessons, 1 quiz
Module 6 – Leading HITL Operations
Module 6 – Overview
Team Coordination and Workflow Assignment
How to Supervise Annotators
Productivity Optimization
Handling Client Feedback
Building Scalable HITL Systems
Module 6 – Knowledge Check Quizzes
7 lessons, 1 quiz
Module 7 – Final Project & Assessment (Hand-on Lab)
Module 7 – Overview
1 lesson
Module 2 – Quality Control (QC) Techniques
First-Pass vs Second-Pass QA
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