Nuv Ahuja

Key Skills

Software

AWS SageMaker

Anno-Mage

Argilla

Axiom AI

Clickworker

CloudFactory

Data Annotation Tech

Datatroniq

Datature

Deep Systems

Google Cloud Vertex AI

HiveMind

Label Studio

LightTag

Mighty AI

OpenCV AI Kit (OAK)

Redbrick AI

Remotasks

SuperAnnotate

Toloka

Other

Scale AI

Labelbox

Top Subject Matter

Computer Vision/Image Recognition

Natural Language Processing (NLP)

Healthcare and Medical Data

Top Data Types

3D Sensor

Computer Code Programming

Geospatial Tiled Imagery

Top Task Types

Classification

Computer Programming Coding

Data Collection

Diagnosis

Translation Localization

Freelancer Overview

As a dedicated Data Labeling Specialist, I have extensive experience in annotating complex datasets for AI and machine learning models. My expertise spans across various domains, including computer vision, natural language processing (NLP), and healthcare. I am proficient in using industry-standard tools like Labelbox and Supervisely, ensuring high accuracy and consistency in data annotation. My meticulous attention to detail and strong analytical skills enable me to contribute significantly to the development of reliable and high-performing AI systems. Throughout my career, I have successfully completed numerous data labeling projects, ranging from image classification and object detection to sentiment analysis and entity recognition. My ability to collaborate effectively with data scientists and engineers has been crucial in maintaining high standards of data quality and meeting project deadlines. With a solid foundation in machine learning principles and a passion for leveraging AI to solve real-world problems, I am committed to delivering precise and valuable training data that drives innovation and excellence in AI applications.

Labeling Experience

Medical Image Annotation for Disease Detection

LabelboxImageSegmentationClassification

This project involved annotating a large dataset of medical images, including X-rays, MRIs, and CT scans, for the purpose of training AI models to detect and classify various diseases. As the lead Data Labeling Specialist, I collaborated with a team of medical professionals and data scientists to ensure that the annotations were accurate and clinically relevant. The labeling tasks included object detection to identify specific areas of interest, image segmentation to delineate the boundaries of anomalies, and classification to categorize the type of disease present. My meticulous approach and attention to detail were critical in achieving high-quality annotations that significantly improved the model's diagnostic accuracy.

This project involved annotating a large dataset of medical images, including X-rays, MRIs, and CT scans, for the purpose of training AI models to detect and classify various diseases. As the lead Data Labeling Specialist, I collaborated with a team of medical professionals and data scientists to ensure that the annotations were accurate and clinically relevant. The labeling tasks included object detection to identify specific areas of interest, image segmentation to delineate the boundaries of anomalies, and classification to categorize the type of disease present. My meticulous approach and attention to detail were critical in achieving high-quality annotations that significantly improved the model's diagnostic accuracy.

2023 - 2023

Sentiment Analysis for Customer Feedback

Aws SagemakerTextEntity Ner ClassificationSegmentation

In this project, I was responsible for annotating a large dataset of customer feedback, including reviews and survey responses, to train a sentiment analysis model. The goal was to classify the sentiment expressed in each text (positive, negative, or neutral) and identify key entities mentioned, such as product names and service attributes. By leveraging tools like Amazon SageMaker Ground Truth and Prodigy, I ensured precise and consistent annotations. This project required a deep understanding of linguistic nuances and the ability to distinguish subtle emotional cues in text. The resulting model provided valuable insights into customer satisfaction and helped the company improve its products and services.

In this project, I was responsible for annotating a large dataset of customer feedback, including reviews and survey responses, to train a sentiment analysis model. The goal was to classify the sentiment expressed in each text (positive, negative, or neutral) and identify key entities mentioned, such as product names and service attributes. By leveraging tools like Amazon SageMaker Ground Truth and Prodigy, I ensured precise and consistent annotations. This project required a deep understanding of linguistic nuances and the ability to distinguish subtle emotional cues in text. The resulting model provided valuable insights into customer satisfaction and helped the company improve its products and services.

2022 - 2022

Education

Work History