Data Labeling Pipeline

A complete toolkit for managing ML data labeling workflows — from annotator assignment through quality auditing to model-ready export. Includes inter-annotator agreement metrics (Cohen's κ, Fleiss' κ,

Product Content

Browse the actual product documentation and code examples included in this toolkit.

Key features of Data Labeling Pipeline

Code

• Active learning selectors — Uncertainty, margin, entropy, random baseline, and query-by-committee strategies. Feed in `predict_proba` output, get back the most informative sample indices.
• Label quality auditing — Annotator scorecards (agreement, speed, bias), suspect label detection, distribution drift testing (chi-squared), and automated audit reports.
• Schema management — Define labeling taxonomies with categories, hierarchy, colors, and keyboard shortcuts. Validate annotations against schemas. Version and migrate schemas over time.
• Workflow engine — Task creation, batch assignment (round-robin and difficulty-stratified), label submission, consensus resolution (majority vote / unanimous), adjudication, and training data export.
• Labeling UI template — Ready-to-deploy HTML/CSS/JS annotation interface with keyboard shortcuts, progress tracking, and guidelines sidebar.

Active learning selectors — Uncertainty, margin, entropy, random baseline, and query-by-committee strategies. Feed in `predict_proba` output, get back the most informative sample indices.

Label quality auditing — Annotator scorecards (agreement, speed, bias), suspect label detection, distribution drift testing (chi-squared), and automated audit reports.

Schema management — Define labeling taxonomies with categories, hierarchy, colors, and keyboard shortcuts. Validate annotations against schemas. Version and migrate schemas over time.

Workflow engine — Task creation, batch assignment (round-robin and difficulty-stratified), label submission, consensus resolution (majority vote / unanimous), adjudication, and training data export.

Labeling UI template — Ready-to-deploy HTML/CSS/JS annotation interface with keyboard shortcuts, progress tracking, and guidelines sidebar.

Interactive Preview

Configure Data Labeling Pipeline parameters to see how the product works.

Active Uncertainty Feed

Quick Start:

python -m src.agreement          # Shows kappa computations
python -m src.active_learning    # Shows strategy comparison
python -m src.quality_audit      # Shows audit report + drift detection
python -m src.schema             # Shows schema management
python -m src.workflow            # Shows full w

Key Features:

Active learning selectors — Uncertainty, margin, entropy, random baseline, and query-by-committee strategies. Feed in `predict_proba` output, get back the most informative sample indices.
Label quality auditing — Annotator scorecards (agreement, speed, bias), suspect label detection, distribution drift testing (chi-squared), and automated audit reports.
Schema management — Define labeling taxonomies with categories, hierarchy, colors, and keyboard shortcuts. Validate annotations against schemas. Version and migrate schemas over time.
Workflow engine — Task creation, batch assignment (round-robin and difficulty-stratified), label submission, consensus resolution (majority vote / unanimous), adjudication, and training data export.
Labeling UI template — Ready-to-deploy HTML/CSS/JS annotation interface with keyboard shortcuts, progress tracking, and guidelines sidebar.

Get the Full Data Labeling Pipeline

This demo shows a preview. The full version includes complete source code, documentation, and lifetime updates.

Buy Full Version — $24.00

🤖 Machine Learning — Data Labeling Pipeline Demo