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Paul Pokotylo

Paul Pokotylo

Paul Pokotylo is an Account Executive, leading strategic initiatives in accelerating AI development. He specializes in custom data solutions for various business applications that drive value.

Data Annotation

Using Synthetic Scenarios for Model Validation and Stress Testing

Model validation and stress testing have become essential in modern data-driven decision-making in various industries, from finance to autonomous systems. As models grow in complexity and are deployed in high-stakes environments, assessing their reliability under diverse and unpredictable conditions becomes even more critical. One method that has emerged to support

Data Annotation

Reducing the cost of annotation: automation, active learning, and more

Automation and active learning allow organizations to scale their efforts while scaling down annotation costs and time, maintaining high quality. Combining cutting-edge technologies with expert review allows for the best results. Active learning enables companies to perform highly with less data, resulting in fewer annotations and faster deployment times. Next,

Computer Vision

Seamlessly Integrate Annotation Stages into Your Data Pipeline

Most data issues arise from incorrect or incomplete annotations. This shows how important seamless annotation integration is in today’s data workflows. Data pipelines are now the backbone of analytics, business intelligence, and AI training. Integrating annotation stages into your data pipeline transforms raw data into actionable insights. This will

Computer Vision

Intellectual Property Considerations in Data Annotation

Intellectual property (IP) issues in data annotation are essential to address, especially as annotated datasets become central to the development of artificial intelligence systems. The first issue is who owns the original data. Whether it's text, images, video, or audio, this data often comes from sources with copyright

Data Labeling

Precision and Recall for Evaluating Annotation Quality

In machine learning and data annotation, precision and recall are important metrics that help us better understand an AI model's performance. They are tools for balancing positive and negative outcomes. By understanding the conceptual and mathematical foundations of precision and recall, you will understand their role in improving

Computer Vision

Creating Feedback Loops: Using Annotation Insights to Improve Models Iteratively

Feedback helps teams work in a coordinated manner, minimizing misunderstandings and ensuring that each iteration leads to improved results. A key benefit of such feedback is the rapid detection and correction of errors, allowing models to become more accurate with each iteration. Effective feedback improves the quality of work on

Machine Learning

Prompt-Based Annotation: Streamlining NLP Labeling at Scale

Natural language processing (NLP) technologies are developing rapidly, and thanks to large language models (LLMs), text labeling is reaching a new level, combining speed, scalability, and accuracy. One of the most advanced approaches is prompt-based annotation. It allows you to use prompts (prompts) to guide models in different NLP tasks