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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.

Outsourcing vs. In-House Labeling: A Cost-Benefit Analysis

Outsourcing vs. In-House Labeling: A Cost-Benefit Analysis

Data labeling is a fundamental step in machine learning and artificial intelligence that directly affects models' performance. Companies must choose between outsourcing this task to specialized vendors or building internal teams to perform it themselves. Budget, project scope, timeline, and available expertise often influence this decision. While both options

Machine Learning

Labeling Data for Industrial Robotics: Enhancing Automation Accuracy

The drive for more accurate and efficient automation continues to accelerate in the rapidly changing landscape of industrial robotics. Factories and production lines increasingly rely on sophisticated robotic systems to perform complex tasks requiring precision and speed. As these systems become more refined, the interaction between the machines and their

Computer Vision

Zero- and Few-Shot Labeling: The Future of Data Annotation

Modern methods that combine pre-trained language models with transfer learning allow systems to adapt to new tasks with minimal annotated data. Rather than creating datasets from scratch, modern architectures reuse learned patterns to solve new tasks. This approach reduces annotation costs while maintaining competitive accuracy rates. These innovations are transforming

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