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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.

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

Data Annotation

Using Predictive Analytics to Forecast Annotation Needs and Timelines

Predictive annotation analytics is changing the way companies manage their data annotation projects. Analyzing historical data can predict future annotation requirements. This ensures that resources are used correctly and deadlines are met without compromising quality. This improves machine learning models and the preparation of datasets for AI training. Predictive analytics

Machine Learning

LiDAR Annotation Techniques: Building Robust Autonomous Navigation Models

The discovery of the laser in 1960 radically changed the field of rangefinding, providing more accurate and detailed measurements over long distances. The first system used a ruby laser that emitted light pulses reflected from an object and returned. By determining the time the pulses traveled to the target and

Data Annotation

Measuring Inter-Annotator Agreement: Building Trustworthy Datasets

If the data annotations do not match between different annotators, it can affect the accuracy of the model results. These datasets must be accurate because they directly affect AI systems' performance. Studying the agreement between annotators helps not only to improve learning models but also to make artificial intelligence

Machine Learning

Building Annotator Training Programs for Better Results

Developing annotator training programs is needed to train people to accurately annotate data, follow strict instructions, and adapt to new projects. It will increase the accuracy of AI models and improve user interactions in areas such as healthcare, autonomous driving, and customer service. AI-powered data processing and step-by-step model evaluation

Machine learning

Streamlining Continuous Training Data Refresh in MLOps Workflows

Methods such as continuous integration (CI), continuous delivery (CDD), and continuous workflow (CT) improve machine learning efficiency. These approaches help improve predictions' accuracy and AI systems' performance, which is especially important in fields that deal with large data sets and complex calculations. These methods simplify the operations of