Data entry ops · Production

How Walmart uses Labelbox data to improve their natural language models

The problem

Walmart's data science team needed faster ways to annotate conversational text from shopping chatbots and label inventory images, but their existing tech-enabled BPO vendors operated as a black box with poor transparency, sparse visibility into data quality, and no tooling for stakeholder collaboration.

First attempt

Walmart previously relied on tech-enabled BPOs for labeled training data, but the arrangement proved suboptimal: BPOs lacked transparency, omitted key analytics, and prevented in-house subject matter experts from closely collaborating with external service providers.

Workflow diagram · grounded in source
1
Label data need identified
trigger
“The data science team wanted to find faster ways to annotate conversational text from shopping chatbots and label inventory images for their object detection and classification models, which included tens of millions of diverse product SKUs”
2
BigQuery workflow initiation
integration
“This allowed labeling workflows to be initiated from Google BigQuery, which the enterprise heavily relied on, and had set up as a core part of its existing data infrastructure. Labels could now be easily pulled and pushed from BigQuery t…”
3
Model-assisted pre-labeling
ai_action
“Model-based pre-labeling (also known as model-assisted labeling) also sped up their labeling process by allowing their team to adjust annotations as opposed to creating ground-truth labels from scratch”
4
Reviewer quality check
validation
“reviewers could check and ensure quality benchmarks in training data were being met”
5
Labeled data output
output
“The company is now able to draw insights about labeling performance, taking actions that directly translates into improvements in label throughput, efficiency and quality”
Reported outcome

Walmart saw labeled data accuracy improve by an estimated 25%, achieved 95% accuracy in labeled data via Labelbox's Labeling Services, and reduced turnaround time by 25% compared to similar services, with full visibility into the labeling pipeline.

Reported metrics
Labeled data accuracy improvement25%
Labeled data accuracy delivered95%
Turnaround time reduction25%
Reported stack
LabelboxLabelbox AnnotateLabelbox Python SDKNERGoogle BigQueryGoogle Cloud
Source
https://labelbox.com/customers/walmart-customer-story/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Walmart saw labeled data accuracy improve by an estimated 25%, achieved 95% accuracy in labeled data via Labelbox's Labeling Services, and reduced turnaround time by 25% compared to similar services, with full visibil…

What tools did this team use?

Labelbox, Labelbox Annotate, Labelbox Python SDK, NER, Google BigQuery, Google Cloud.

What results were reported?

Labeled data accuracy improvement: 25%; Labeled data accuracy delivered: 95%; Turnaround time reduction: 25% (source-reported, not independently verified).

What failed first in this deployment?

Walmart previously relied on tech-enabled BPOs for labeled training data, but the arrangement proved suboptimal: BPOs lacked transparency, omitted key analytics, and prevented in-house subject matter experts from clos…

How is this data entry ops AI workflow structured?

Label data need identified → BigQuery workflow initiation → Model-assisted pre-labeling → Reviewer quality check → Labeled data output.

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