Data entry ops · Production

Leading e-commerce company achieves 50% labeling efficiency gain with Labelbox model-assisted labeling

The problem

A Fortune 500 e-commerce enterprise needed large volumes of high-quality labeled training data for image classification and object detection models across tens of thousands of product SKUs. Their prior AI-based labeling service consistently failed to meet quality requirements, blocking AI development.

First attempt

The prior data annotation service that used AI to generate labels consistently failed to meet the team's labeling quality requirements.

Workflow diagram · grounded in source
1
Labeling request intake and routing
routing
“the enterprise set up a single point of contact inside its data science team to gather information about each use case, review and approve labeling projects, and transfer approved projects to the Labelbox Services team”
2
GCP and BigQuery data import
integration
“simplify the data import process from Google Cloud (GCP), which was set up as a core part of their existing data infrastructure. Labels can now be easily pulled and pushed from BigQuery tables”
3
Model-assisted pre-labeling
ai_action
“leveraged model-assisted labeling for their object detection labeling needs. The team had one dataset labeled traditionally, used it to train a baseline model, and then used the model's output as pre-labels for subsequent batches of data…”
4
Human review and correction
human_review
“the labeling team only had to review and correct the model's labels, increasing efficiency by roughly 50%, while maintaining a high standard for data quality”
5
Weekly feedback loop
feedback_loop
“the team would review a sample of labeled data on a weekly basis, creating a reliable feedback loop to evaluate and continuously increase AI data quality. This extra investment helped the labelers improve their work and reduced the itera…”
6
Vertex AI model training
integration
“model training can now be easily integrated by connecting complex training jobs to Google's Vertex AI for optimization”
Reported outcome

The enterprise significantly improved AI training data quality, unblocked its AI initiatives, and increased labeling speed and efficiency by 50% without compromising data quality.

Reported metrics
Labeling speed and efficiency50%
AI training data qualitysignificantly improved
AI initiatives statussuccessfully unblocked
Reported stack
Labelbox AnnotateLabelbox Labeling ServicesPython SDKGoogle CloudBigQueryVertex AI
Source
https://labelbox.com/customers/ecommerce-shopping-customer-story
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

The enterprise significantly improved AI training data quality, unblocked its AI initiatives, and increased labeling speed and efficiency by 50% without compromising data quality.

What tools did this team use?

Labelbox Annotate, Labelbox Labeling Services, Python SDK, Google Cloud, BigQuery, Vertex AI.

What results were reported?

Labeling speed and efficiency: 50%; AI training data quality: significantly improved; AI initiatives status: successfully unblocked (source-reported, not independently verified).

What failed first in this deployment?

The prior data annotation service that used AI to generate labels consistently failed to meet the team's labeling quality requirements.

How is this data entry ops AI workflow structured?

Labeling request intake and routing → GCP and BigQuery data import → Model-assisted pre-labeling → Human review and correction → Weekly feedback loop → Vertex AI model training.

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