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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.

Annotating Sensor Data for Utility Equipment Failure Prediction

Annotating Sensor Data for Utility Equipment Failure Prediction

The study of equipment reliability in utility systems increasingly relies on structured annotation of sensor data. Signals collected from transformer health monitoring, vibration analysis, and infrared imagery require systematic labeling to establish a direct connection between measurement patterns and specific failure modes. Without annotation, fluctuations in sensor outputs remain ambiguous,

Edge AI: Cameras and Annotations

Edge AI: Cameras and Annotations

Businesses still rely on manual visual inspections for quality control. This is now replaced by intellectual systems that analyze visual inputs in the source and combine deep training with decentralized processing. Traditional cloud solutions have problems with delay and bandwidth restrictions. Modern approaches unfold a curtain neural network (CNN) on

Edge AI Data Abstract: Optimizing Models for Devices

Edge AI Data Abstract: Optimizing Models for Devices

Most real-time device decisions are now made without the cloud. This is due to the breakthrough in machine learning workflows prioritizing speed and autonomy. Traditional cloud-dependent systems struggle to meet today's demands for instant responsiveness in medical diagnostics and autonomous vehicles. The strategic benefits go beyond speed. By

RLHF Annotation Services: Training AI Models with Human Feedback

RLHF Annotation Services: Training AI Models with Human Feedback

Traditional language models rely on text patterns. Modern systems need to understand intent, context, and ethics. This requires a three-phase process: basic pretraining, supervised refinement, and reinforcement learning optimization. Each phase integrates expert human judgment to align results with real-world needs. Combining computational power with practical application through precise feedback

Tools and Tips for Remote Collaboration in Annotation Teams

Tools and Tips for Remote Collaboration in Annotation Teams

Remote collaboration in annotation teams has become essential as organizations increasingly rely on distributed workforces. Annotation projects often involve complex datasets that require precise communication and real-time feedback. Clear communication protocols, regular check-ins, and defined roles foster accountability and smooth workflows. Training sessions and quality checks support consistent standards across

Reducing Manual Labeling Effort Through Smart Sample Selection

Reducing Manual Labeling Effort Through Smart Sample Selection

Reducing the manual labeling required in machine learning pipelines has become an increasingly important goal as data continues to scale in volume and complexity. One promising approach to alleviate this burden is strategic data sampling, where only the most informative or influential examples are selected for labeling. This approach, rather

Establishing Performance Benchmarks for Annotation Teams

Establishing Performance Benchmarks for Annotation Teams

A large number of AIs fail due to poor data integrity. This confirms the important role of performance benchmarks in data annotation. Establishing performance benchmarks for annotation teams is imperative. They show annotators' progress on projects and help track their performance. Clear metrics and standards increase the efficiency of