Technical Writing & Case Studies
Engineering notes, architectural choices, research findings, and technical breakdowns based on my actual AI/ML work across Computer Vision, Generative AI, NLP, and Medical AI research.
Evaluating object detection and tracking consistency across continuous endoscopic video sequences. (Ongoing B.Tech final-year research project).
A technical case study on using YOLOv8 instance segmentation for automatic landslide boundary detection, risk classification, and real-time hazard monitoring.
Insights into training stability, Wasserstein loss, gradient penalty implementation in TensorFlow, and evaluating generative quality using FID and Inception Score.
Architecture and engineering behind SmartQ: combining T5 transformers, Speech-to-Text (STT), multi-language translation, and Text-to-Speech (TTS) into a unified pipeline.
Practical strategies for detecting and avoiding target leakage and client-level feature overlap in large-scale datasets, drawing from real-world ML experience on 78.8M search performance records.
Combining EasyOCR text extraction, fast rule-based regex parsers, and LLM fallback engines into a production FastAPI microservice for multi-format invoice processing.
Bridging standalone deep learning perception models with real-world robot actuation using ROS2 nodes, pub/sub topics, and sensor fusion.
A structured engineering framework: framing business metrics, diagnostic EDA, robust cross-validation, baseline selection, hyperparameter tuning, and containerized deployment.