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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.

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

Computer vision

Mitigating Annotation Bias in AI Models

Reducing bias in datasets is essential for improving AI performance. Bias in machine learning can introduce systematic errors and inaccurate results. We will discuss ways to improve fairness in AI. The harmful effects of biased annotation are observed in various fields. For example, incorrectly annotated data can reinforce stereotypes and

Computer vision

Integrating MLOps into Your Data Annotation Pipeline for Continuous Model Improvement

Integrating MLOps into your data annotation pipeline is key for ongoing model enhancement. MLOps, or Machine Learning Operations, integrates machine learning models into production. It manages their lifecycle. MLOps can automate and improve data annotation. This will boost data quality and maximize our models' performance. We aim to build

Data Annotation

The Future of Mobility: AI in Automotive and Smart Transportation

AI in Automotive and Smart Transportation isn't just making driving better but also improving manufacturing, predictive maintenance, and fleet management. It's transforming transportation into a safer, more efficient, and sustainable digital space. AI systems analyze data in real-time to predict vehicle issues, ensuring reliability and safety.