AI Training Data Specialist (Project-Based Training)

Murtadah Alamer
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LinkedIn Learning | Course by Wuraola Oyewusi
Sep 2025

Core Achievement: Completed a comprehensive, project-based training program focused on applied data annotation for computer vision and NLP. Gained practical, hands-on experience across the entire annotation lifecycle, from data setup to quality assurance, using a wide range of industry-standard platforms and tools.

Key Accomplishments & Skills Gained:

  1. Multi-Platform Proficiency: Successfully executed end-to-end annotation projects on major cloud platforms (AWS SageMaker, Azure ML) and open-source tools (CVAT, Roboflow), demonstrating the ability to quickly adapt to different UIs and workflows.

  2. Computer Vision Annotation: Applied a range of techniques to a 12-image “Cars on the Street” dataset, including object detection (labeling 80+ instances of cars with bounding boxes), object classification (labeling 100+ images of boots or sneakers), and instance segmentation (creating precise polygon masks). Also completed a 45-image single-label classification project.

  3. NLP & Audio Annotation: Performed core NLP annotation tasks on a sample text corpus, including Named Entity Recognition (NER) (labeling 15+ entities like ORG and PERSON) and sentiment analysis. Also practiced audio transcription and diarization on a multi-language audio file.

  4. Model-Assisted Labeling: Increased annotation efficiency by leveraging AI-powered tools, using a YOLOv7 model in CVAT and Label Assist in Roboflow to generate initial labels for review and correction. Also practiced with the advanced Segment Anything Model (SAM) for high-quality, semi-automated polygon masking.

  5. Formats: Successfully exported final datasets in multiple industry-standard formats, including COCO (JSON), Pascal VOC (XML), and YOLO (TXT).

Skills: Cloud Annotation Platforms (AWS SageMaker, Azure ML) · Data Annotation Tools (CVAT, Roboflow, UDT) · Computer Vision (Object Detection, Instance Segmentation) · NLP (NER, Sentiment Analysis) · Model-Assisted Labeling · Annotation Formats (COCO, YOLO).

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