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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.

Data Anonymization Strategies: Protecting User Identity in Annotations

Data Anonymization Strategies: Protecting User Identity in Annotations

In today's data-driven world, ensuring the privacy and security of user data is becoming increasingly important, especially in tasks that involve annotating sensitive information. Data anonymization strategies play a key role in this effort by offering practical approaches to removing personal information while maintaining the usefulness of the

Trend Analysis of Labeling Errors: Improving Over Time

Trend Analysis of Labeling Errors: Improving Over Time

Understanding how labeling errors change over time can reveal a lot about a machine learning pipeline's strengths and weaknesses. Analyzing the trends of these errors helps teams track whether their labeling methods are improving, staying consistent, or deteriorating as the dataset grows. It can also reveal patterns related

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