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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Automatic data labeling can save time by generating labels for routine examples and directing people to uncertain ones. It can lower costs when that reduces manual annotation, review, or redundant data processing. The gains depend on the data and workflow: people still need to define labels, check quality, and handle cases automation gets wrong.
1. It reduces time spent labeling routine examples
One approach is to train a model on a small set of human-labeled examples, then use its confidence scores to label likely matches automatically and send uncertain cases to people. This is often called active learning or model-assisted labeling. It shifts human effort toward examples that are informative or need judgment rather than asking annotators to label every record from scratch.
Samsung SDS says its autoLabel workflow may require people to label 5%–16% of the dataset before confidence is high enough to label the remainder automatically. The company also says domain experts can check those automatic labels with over 80% less effort than creating labels from scratch. These are Samsung SDS’s product claims; they are not general benchmarks. Its page also reports 66% less time to start labeling and up to 54% lower total cost of ownership, depending on use case. The page’s publication year is not stated; accessed in 2026. Samsung SDS autoLabel
Automation does not always require a trained model. Programmatic labeling uses rules, existing taggers, keywords, topic models, or knowledge sources to assign labels at scale. In a Snorkel AI customer story, Google used such labeling functions to label 684,000 data points for one topic classifier in a few minutes and 6.5 million for a product classifier in 30 minutes. These are figures from that specific customer example, not a result every project should expect. The story notes related work published in SIGMOD in 2019 and VLDB in 2020; those publications should not be treated as the source of the case-study figures. Snorkel AI’s Google case study
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A UK Government Analysis Function project used a large language model (LLM) to process regulatory metadata. It reports that the LLM processed 73% of documents in 20 seconds or less, and all documents in under 120 seconds, compared with an average human tagging time of 318 seconds per document. Those numbers describe that project’s documents and tagging process, not a universal speed comparison. Human taggers checked the outputs because LLMs can hallucinate. The report says the goal was to free people for more useful work, not replace them. UK Government Analysis Function project report
2. It can reduce annotation and processing costs
Costs can fall when annotators review model suggestions instead of creating every label, or when automated processing eliminates unnecessary work such as checking duplicate data. The savings must be weighed against setup and operating costs, including data preparation, label definitions, model or rule development, cloud or GPU use, integrations, monitoring, and human review.
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Model-assisted review can reduce annotation effort
Labelbox’s Sharper Shape customer story describes contributors focusing on reviewing false positives rather than grading every example from scratch. Labelbox reports that this approach cut average training-data creation costs by as much as 50% while maintaining what the customer described as high-quality signal; it also reports model training sped up by over 10x. These are Labelbox’s figures for that customer workflow, not a general cost guarantee. Labelbox’s Sharper Shape story
Automated curation can reduce manual processing
NVIDIA’s FastLabel story describes an image workflow using NeMo Curator, image captioning, embeddings, semantic deduplication, and cloud GPU processing. NVIDIA reports that captioning 10,000 images took about 14.6 hours, compared with 333 hours of prior manual effort. Text embedding took six minutes, and semantic deduplication took four minutes. The story puts the end-to-end process at less than $57 per 10,000 images and the core deduplication step at $0.26 on an A100 GPU. These figures apply to FastLabel’s described setup; they are not a typical price quote or a general cost estimate. NVIDIA’s FastLabel case study
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Scaling a labeling operation may avoid long manual timelines
AWS’s customer page quotes Krikey CEO Jhanvi Shriram saying the company scaled from 100 to 100,000 labeled videos in one month rather than one year using SageMaker Ground Truth Plus. Shriram estimated savings of 1,000 data-scientist hours and $200,000. This is a reported customer outcome and estimate, not an independent cost audit. AWS SageMaker Ground Truth customer page
Why human review still matters
Automated labels can be wrong, especially for ambiguous, rare, or poorly represented examples. A useful workflow assigns people responsibility for defining what each label means, checking outputs, correcting errors, and deciding when a model or rule should not be trusted. Samsung SDS’s described process has people label informative examples and check the automated results; the UK government project likewise retained human quality control. In AWS’s SageMaker Ground Truth customer page, AI21 Labs co-founder and co-CEO Ori Goshen says, “It’s always important to have human validation, or a human in the loop, that helps you steer the models toward the right direction.” AWS SageMaker Ground Truth customer page
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How to judge whether automation will save your team money
Do not compare model inference time with the time it takes a person to label one item and call that the full saving. Compare the complete workflow on the same data and with the same definition of an accepted label:
- End-to-end time: Include setup, annotation, review, corrections, and rework—not only automated processing.
- Total cost: Count labor, implementation, compute, integrations, monitoring, and ongoing quality checks.
- Quality and error handling: Measure the accuracy of accepted labels and how errors, ambiguous examples, and rare cases are caught.
- Scale and data type: Confirm that the approach supports your volume and modality, such as text, images, or video.
- Auditability: Check whether people can inspect, correct, and trace how labels were assigned.
The cited examples use different datasets, label definitions, workflows, and cost baselines, so they are not a controlled head-to-head comparison. They illustrate ways automation can save effort, not a universal return on investment or a promise that every labeling task is suitable for automation.
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