Data Labeling & AI Training
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Data CollectionWorked on AI and Machine Learning data preparation projects involving the annotation, classification, and validation of large datasets used to train and improve AI models. The project focused on ensuring high-quality labeled data for natural language processing (NLP), computer vision, and content moderation applications. Scope of the Project Processed and annotated datasets to support AI model training and evaluation. Reviewed and categorized text, image, and audio data according to predefined guidelines. Assisted in improving model accuracy by identifying labeling inconsistencies and correcting errors. Participated in quality assurance and validation tasks to maintain dataset integrity. Specific Data Labeling Performed Text Annotation: Sentiment analysis, intent classification, topic categorization, and AI response evaluation. Image Annotation: Object identification, image classification, tagging, and bounding box labeling. Content Moderation: Identification and categorization of harmful, inappropriate, or policy-violating content. Data Verification: Cross-checking labeled data for consistency, completeness, and accuracy. AI Training Feedback: Evaluating AI-generated responses and providing ratings based on relevance, accuracy, and quality. Project Size Annotated and reviewed over 50,000+ data points across multiple projects. Processed datasets consisting of text, image, and multimedia content. Met daily and weekly productivity targets while maintaining quality standards in a remote work environment. Quality Measures Adhered To Maintained 95%+ labeling accuracy through adherence to detailed annotation guidelines. Conducted regular quality checks and peer reviews to ensure consistency. Followed strict data privacy, confidentiality, and security protocols. Performed validation and error correction before final dataset submission. Ensured compliance with project-specific quality assurance metrics and turnaround time requirements.