AI TRAINING
Data Labeling and Annotation for ML Teams
Build reliable annotation pipelines that produce high-quality training data at scale for ML projects.
What it covers
This programme covers the full annotation lifecycle: from defining labeling schemas and setting up workflows to measuring inter-annotator agreement and managing label quality at scale. Participants learn to evaluate build-vs-buy decisions for annotation tooling, implement active learning strategies to reduce labeling costs, and establish quality control pipelines. Delivered as a mix of instructor-led sessions and hands-on lab exercises using real annotation platforms, the course targets data teams preparing to train or fine-tune production ML models.
What you'll be able to do
- Design a complete labeling schema with clear guidelines, edge-case rules, and quality acceptance criteria for a real dataset
- Calculate and interpret inter-annotator agreement scores and use them to improve annotation consistency
- Configure and run an active learning loop that selects the most informative samples for annotation
- Evaluate and select annotation tooling or vendor partners against defined quality, cost, and compliance criteria
- Implement an automated label-quality audit pipeline that flags and routes problematic annotations for review
Topics covered
- Labeling schema design: classes, ontologies, and edge-case guidelines
- Annotation tooling landscape: open-source vs. managed platforms (Label Studio, Scale AI, Labelbox)
- Inter-annotator agreement metrics: Cohen's Kappa, Fleiss' Kappa, Krippendorff's Alpha
- Active learning strategies to prioritise uncertain or high-value samples
- Label quality auditing and automated error detection
- Vendor evaluation and outsourced annotation workforce management
- Data versioning and lineage for annotated datasets
- Compliance and data privacy considerations in annotation workflows
Delivery
Delivered over 3-4 days (in-person or remote), combining 40% instructor-led instruction with 60% hands-on lab work. Participants work directly in Label Studio and optionally connect to a cloud annotation platform. Each cohort receives a starter dataset and a pre-built annotation project to complete end-to-end. Remote delivery uses shared cloud environments; in-person delivery requires laptop setup. Printed quick-reference cards and a post-training annotation playbook are included.
What makes it work
- Establishing a dedicated annotation quality lead or role before scaling annotation efforts
- Running regular inter-annotator agreement audits throughout the project, not just at kick-off
- Integrating annotation tooling directly into the ML training pipeline for automated dataset versioning
- Starting with a small gold-standard set that annotators can calibrate against before processing the full dataset
Common mistakes
- Defining labeling guidelines too late, after annotators have already developed inconsistent habits
- Treating annotation as a one-time task rather than an iterative quality process tied to model performance
- Outsourcing annotation without establishing clear acceptance criteria or a review workflow, leading to label noise
- Ignoring data versioning for annotated datasets, making it impossible to trace model degradation to labeling changes
When NOT to take this
If a team is still exploring whether to build an ML model at all and has no confirmed dataset, this training is premature, invest first in use-case scoping and data discovery.
Example providers
This page describes a training topic. It gives no price or duration: those depend on the provider and your team. The free diagnostic tells you which trainings your team needs first.