Active Learning and Data Labeling Workflow for Computer Vision (From Pilot to Production)

Key Takeaways

• Active learning reduces labeling cost by focusing on informative samples.
• Capture evidence and metadata from production workflows.
• Use strong versioning for datasets and models.
• Treat labeling and retraining as a routine cycle.

Computer vision systems improve when you make labeling and retraining a routine, not a special event. Active learning is a set of tactics to label the most informative samples first.

This post outlines a practical workflow that integrates with real deployments.

Step 1: Define success metrics and failure modes

Before labeling, define:

• Precision and recall targets
• What false positives cost
• What false negatives cost

Model evaluation references:

• COCO evaluation overview: https://cocodataset.org/#detection-eval

Step 2: Capture evidence from production

Your system should store:

• Sample frames or clips
• Model predictions
• Confidence scores
• Context metadata

Step 3: Select samples for labeling

Useful selection strategies:

• Low confidence samples
• High disagreement between models
• Rare edge cases

Active learning overview:

• https://arxiv.org/abs/2009.00236

Step 4: Label efficiently

Labeling tools:

• CVAT: https://cvat.ai/
• Label Studio: https://labelstud.io/

Step 5: Train, validate, and deploy with versioning

Track:

• Dataset version
• Model version
• Evaluation results

Model management references:

• MLflow: https://mlflow.org/

Step 6: Close the loop with monitoring

Monitor:

• Drift indicators
• Event rates
• Accuracy sampling via spot checks

zed compute.

Internal reference:

• Lid Vizion computer vision services: https://lidvizion.ai/

FAQs

Q: Do we need active learning from day one? Not necessarily. Start with a baseline dataset and capture production evidence. Add active learning once you have enough samples.

Q: How do we prevent label noise? Use clear labeling guidelines, run inter annotator checks, and review a small percentage of labels.

Q: How does this connect to AWS and MongoDB? Store metadata and workflow state in MongoDB, store large artifacts in S3, and run training on managed or containeri

Q: What should we link to internally? A: Link to relevant solution pages like Computer Vision or Document Intelligence, and only link to published blog URLs on the main domain. Avoid staging links.