Data annotation & labeling blog.

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Maxim Dupont

Maxim Dupont is the founder of LabelVisor, a scientist & software engineer. His hobby of writing tech reviews has become his new way of life. His belief is that together we can achieve the best world.

Labeling Negative Examples: Teaching Models What Not to Retrieve

Labeling Negative Examples: Teaching Models What Not to Retrieve

In the development of intelligent retrieval systems, much of the focus has traditionally been on teaching models what to look for, using examples that represent relevant, ideal outcomes. Equally important yet often underutilized is showing models what not to retrieve, which relies on negative examples to define the boundaries of

Federated Learning for Privacy-Preserving Data Annotation

Federated Learning for Privacy-Preserving Data Annotation

Federated learning offers a powerful solution for privacy-preserving data annotation by enabling decentralized labeling without transferring raw data to a central server. Instead of aggregating raw data, federated learning relies on secure aggregation techniques that combine model updates from multiple sources to prevent any party from accessing individual contributions. A

Edge AI Model Optimization for IoT Devices: Best Practices in 2025

Edge AI Model Optimization for IoT Devices: Best Practices in 2025

In 2025, the demand for intelligent, autonomous decision-making on low-power endpoints continues to reshape the landscape of edge computing. AI models are no longer confined to cloud infrastructure; they're now embedded within millions of IoT devices, from smart cameras to wearable health monitors. To ensure these devices operate

Annotation Methods for Domain Migration: From Synthetic to Real Data

Annotation Methods for Domain Migration: From Synthetic to Real Data

Synthetic data provides scalability when training AI models. However, real-world applications require accurate annotations that capture unpredictable variables such as lighting or object boundaries. This domain shift, where the source and target data distributions differ, poses challenges in maintaining AI model accuracy. This gap can be bridged by using strategic

Seasonal Adaptation: Labeling Data for Time-Varying Conditions

Seasonal Adaptation: Labeling Data for Time-Varying Conditions

Adapting machine learning models to seasonal or time-varying conditions requires increasing data volume. It demands strategic data labeling that reflects real-world temporal shifts. Seasonal variation introduces complexity through visual or numerical changes and context, such as altered user intent, sensor behavior, or product availability. This can include annotating temporal metadata,

Rapid Prototyping of Datasets: Speeding Up Early-Stage ML Experiments

Rapid Prototyping of Datasets: Speeding Up Early-Stage ML Experiments

In the world of machine learning, early-stage performance determines future success. Dataset prototyping is a strategy that allows teams to quickly test hypotheses, identify data structure errors, and test initial AI models without the need for large amounts of data. With approaches based on rapid dataset creation and minimal labeling,

Rules-Based Tagging for NLP: Rapidly Annotating Text with Pattern Matching

Rules-Based Tagging for NLP: Rapidly Annotating Text with Pattern Matching

In many NLP projects, speed and precision in early-stage annotation can make or break downstream success. A purely machine learning-based approach often falls short when teams face tight timelines, limited training data, or particular labeling needs. Rules-based tagging offers an immediate solution that can be implemented quickly, refined iteratively, and