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Tatiana Verbitskaya

Tatiana Verbitskaya

Tatiana Verbitskaya is a Solutions Engineer dedicated to solving complex problems. In addition to her client-facing work at Keymakr, she is also an active contributor to the computer vision community.

Warehouse and Logistics: Annotating Data for Efficient Inventory Management

Warehouse and Logistics: Annotating Data for Efficient Inventory Management

As businesses grow and customer expectations rise, inventory control becomes challenging and necessary. Technology is increasingly important in improving the tracking, sorting, and movement of goods in these spaces. But for these systems to work well, they need high-quality data that accurately reflects what's happening on the ground.

Prepping Data for Self-Supervised Learning: Labeling Less, Learning More

Prepping Data for Self-Supervised Learning: Labeling Less, Learning More

With datasets' growing complexity and size, the need for methods requiring less manual annotation has become critical. Learning from data without the traditional need for labeled samples pushes the boundaries of autonomous machine capabilities. This approach allows machines to generate their control signals, making it possible to train models

Visualizing Annotation Quality with Heatmaps and Dashboards

Visualizing Annotation Quality with Heatmaps and Dashboards

Ensuring high-quality data is one of machine learning and AI development's most critical but often overlooked aspects. As models grow in complexity and scale, the quality of their training data becomes even more crucial. But how do teams monitor the reliability of annotations across thousands or even millions

Multimodal annotation: combining images, audio, and text for AI models

Multimodal annotation: combining images, audio, and text for AI models

AI models can understand the world like humans by simultaneously processing images, audio, and text data. This innovation is transforming industries from healthcare to autonomous vehicles. Combining multiple data types in multimodal annotation opens up new possibilities in various fields. For example, in e-commerce, AI systems analyze product descriptions and

Managing Distributed Annotation at Scale: Tools and Techniques

Managing Distributed Annotation at Scale: Tools and Techniques

As AI applications grow more sophisticated, the demand for high-quality, accurately labeled datasets has never increased. Scalable data labeling tools are essential for managing distributed annotation workflows, ensuring that your machine learning models are accurate and reliable. Whether you are working with image, video, or audio data, the right approach

Semi-automated labeling: combining human judgment with machine speed

Semi-automated labeling: combining human judgment with machine speed

Data annotation is a key component for developing machine learning models. Semi-automated labeling combines human control with the speed of automated algorithms. This approach simplifies the data annotation process, reducing the laboriousness of manual labeling. Data labeling automation uses consistent and accurate data labeling based on predefined rules and algorithms.

Building Annotation Performance Dashboards for Continuous Improvement

Building Annotation Performance Dashboards for Continuous Improvement

Creating annotation performance dashboards for continuous improvement involves building a system that tracks, analyzes, and visualizes the quality and effectiveness of data annotations over time. These dashboards help teams understand annotation accuracy, consistency, speed, and areas for improvement. For visualization, a dashboard should present clear, interactive charts highlighting trends and