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AWS SageMaker
Anno-Mage
Appen
Argilla
Clickworker
CrowdFlower
Computer Code Programming
Image
Medical DicomI have extensive experience in data labeling and AI training data management, primarily working with large-scale datasets to enhance machine learning model performance. My skills include data annotation, quality assurance, and the development of efficient workflows for labeling high-quality, unbiased datasets. I’ve worked with various data types—including image, text, audio, and video—employing tools like Labelbox, Amazon SageMaker Ground Truth, and custom annotation platforms to streamline the labeling process. A notable project was my role in curating a labeled dataset for a natural language processing model, where I focused on fine-tuning text classification labels to improve accuracy in customer sentiment analysis. What sets me apart is my meticulous approach to quality control and my understanding of data bias and ethics, which helps create fair and representative datasets. I am skilled in managing diverse labeling teams, setting guidelines for consistent annotation standards, and using data validation techniques to enhance overall dataset integrity. My background in AI/ML also allows me to anticipate model training needs and proactively address edge cases during data preparation, ensuring that the final datasets are robust and production-ready
the project was based on image recognistion training at night where a vehicle can easily self drive at night with the help of ai
bachelors in software engineering, software engineering
SYSTEM ANALYST, SUPPORT, TESTING AND IMPLEMENTATION ENGINEER
SYSTEMS AND QUALITY ASSUARANCE ENGINEER
