Boaz Onyango

Key Skills

Software

AWS SageMaker

Appen

Clickworker

CloudFactory

CVAT

Datasaur

Humanatic

Labelbox

Remotasks

Sama

Scale AI

SuperAnnotate

Surge AI

Toloka

V7 Labs

Top Subject Matter

Medical Imaging Analysis and Annotation

Natural Language Processing for Multilingual Text Data

Autonomous Vehicle LiDAR and Image Data Labeling

Top Data Types

3D Sensor

Image

Text

Top Task Types

Computer Programming Coding

Data Collection

Emotion Recognition

Evaluation Rating

Text Generation

Freelancer Overview

Autonomous Vehicle Data Annotation: Led a team in annotating thousands of hours of driving footage and LiDAR data, focusing on object detection and scene segmentation. Healthcare Diagnostics: collaborated on a groundbreaking project for annotating medical imagery, such as MRI and CT scans, aiding in the development of AI-driven diagnostic tools. Multilingual NLP Projects: Worked on several NLP projects requiring text annotation in multiple languages, including sentiment analysis and chatbot training, enhancing language models' understanding and responsiveness. Retail Consumer Behavior Analysis: Involved in video annotation projects analyzing consumer behavior in retail environments, contributing to the development of AI solutions for personalized marketing and inventory management. Agricultural Drone Imagery: Participated in a project for annotating drone-captured agricultural images, aiding in crop health analysis and management strategies for precision agriculture.

Labeling Experience

Data Labelling

CVATImageBounding BoxSegmentation

This project involved the labeling of images for an autonomous vehicle dataset aimed at improving object detection and classification algorithms. The scope included annotating various objects such as pedestrians, vehicles, traffic signs, and obstacles using bounding boxes and classifications. The project comprised over 10,000 images, with quality measures including regular peer reviews and a verification process to ensure annotation accuracy. I was responsible for managing the annotation process, maintaining consistency in labeling standards, and ensuring that the dataset met the required specifications for machine learning training.

This project involved the labeling of images for an autonomous vehicle dataset aimed at improving object detection and classification algorithms. The scope included annotating various objects such as pedestrians, vehicles, traffic signs, and obstacles using bounding boxes and classifications. The project comprised over 10,000 images, with quality measures including regular peer reviews and a verification process to ensure annotation accuracy. I was responsible for managing the annotation process, maintaining consistency in labeling standards, and ensuring that the dataset met the required specifications for machine learning training.

2023 - 2023

Ai Data Trainer

Scale AITextRLHFFine Tuning

In this project, I played a crucial role as an AI Data Trainer, focusing on enhancing the capabilities of large language models (LLMs) in understanding and processing multilingual text data. My responsibilities included: Entity Recognition and Classification: Annotating large datasets for named entity recognition (NER), helping the LLM distinguish and classify names, organizations, locations, and other entities in text across multiple languages. Text Summarization and Generation: Training the model on summarizing lengthy documents and generating coherent, contextually relevant text. Emotion and Sentiment Analysis: Labeling text data for emotional undertones and sentiment, enabling the LLM to understand nuanced human expressions in different languages. Question Answering System Training: Annotating datasets for question answering tasks, improving the model's ability to comprehend and respond accurately to a wide range of queries. Extensive Use of Scale AI: Leveraging Scale AI's advance

In this project, I played a crucial role as an AI Data Trainer, focusing on enhancing the capabilities of large language models (LLMs) in understanding and processing multilingual text data. My responsibilities included: Entity Recognition and Classification: Annotating large datasets for named entity recognition (NER), helping the LLM distinguish and classify names, organizations, locations, and other entities in text across multiple languages. Text Summarization and Generation: Training the model on summarizing lengthy documents and generating coherent, contextually relevant text. Emotion and Sentiment Analysis: Labeling text data for emotional undertones and sentiment, enabling the LLM to understand nuanced human expressions in different languages. Question Answering System Training: Annotating datasets for question answering tasks, improving the model's ability to comprehend and respond accurately to a wide range of queries. Extensive Use of Scale AI: Leveraging Scale AI's advance

2023 - 2023

Education

Work History