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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.

Backing Up and Recovering Annotated Datasets: A Practical Guide

Backing Up and Recovering Annotated Datasets: A Practical Guide

Annotated data sets are the driving force behind machine learning and require robust protection, but many teams ignore important precautions. This leaves organizations vulnerable to delays, compliance risks, and eroded customer trust. Strong data backup strategies are a key component of disaster preparedness, helping organizations avoid delays, compliance risks, and

Conduct regular annotation audits

Conduct regular annotation audits

In modern AI systems, annotated data quality impacts models' accuracy, security, and reliability. Mislabeled or inconsistent data can cause prediction bias, and organizations should implement annotation auditing as a standard part of their workflow. Quick Take * Audit preparation involves establishing infrastructure, implementing security protocols, and focusing on proven practices.

Benchmarking Annotation Quality Against Industry Standards

Benchmarking Annotation Quality Against Industry Standards

Annotation quality impacts AI performance, but teams lack clear standards for evaluating their performance. Without reliable metrics, even small mistakes can lead to costly failures. Aligning annotation practices with proven metrics can improve the AI model evaluation process. Analyzing real-world trends can help identify gaps and improve workflows. Accurate comparisons

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