Michael Abramov

Pay-per-label pricing is an approach in which customers are charged based on each annotation applied to a dataset rather than on a service package or fixed subscription fee basis. It differs from flat-rate models in that costs are precisely aligned with the volume and level of detail of annotations, making

Prioritizing complex examples focuses on areas where AI models have difficulty. Instead of randomly selecting data, data collection functions identify cases with high uncertainty through metrics such as entropy or margin. This helps teams reduce annotation overhead and improve productivity. Quick Take * Uncertainty sampling prioritizes data points where AI models

Smart carts, powered by retail AI, are equipped with cameras that identify and classify products on smart shelves for better accuracy and efficiency. These advances are not theoretical. Retailers implementing AI-based solutions increase sales conversion rates and improve inventory tracking accuracy. Quick Take * Deep learning models control inventory and reduce

With the increasing complexity of manufacturing processes, there is a growing reliance on advanced techniques to identify defects early in the production cycle. Annotation techniques play a pivotal role in this process by providing structured data that helps train machine learning models to detect defects automatically. These techniques involve labeling

Data augmentation is a technique that artificially expands datasets and creates different variations of samples without the need for additional annotations. In deep learning, data augmentation increases the amount of data, sets the limits of the AI model's solution, and improves performance. Implementing data augmentation strategies reveals the

When building datasets, annotation teams often come from different cultural and linguistic backgrounds. This diversity can provide valuable perspectives but also leads to differences in how people understand the same task. Language is also closely related to the local context. Annotators may understand words but not their meanings if they