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AWS SageMaker

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Freelancer Overview

I have experience in data labeling and AI training data, particularly in preparing high-quality datasets for machine learning and deep learning projects. My work has involved curating multimodal datasets (including time series, text, and graph data), designing annotation guidelines, and ensuring consistency across large-scale labeling efforts. I am familiar with both manual and semi-automated labeling pipelines, leveraging tools like Python, Pandas, and custom scripts to preprocess raw data into structured formats suitable for model training. I also have hands-on experience with quality assurance processes, such as cross-validation, inter-annotator agreement, and statistical checks to maintain dataset reliability. What sets me apart is my background in both data engineering and AI research. Beyond labeling, I’ve worked on developing interpretable machine learning models, creating benchmarks for time-series reasoning, and building pipelines for graph-based and spatiotemporal data. This dual perspective allows me not only to ensure precise labeling but also to anticipate how labeled data impacts downstream model performance. My skills in Python, data preprocessing, and large-scale dataset management, combined with my research focus on fairness and interpretability, enable me to contribute to AI training data projects with both rigor and innovation.

Labeling Experience

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