My technical depth spans the full ML pipeline — from raw data to deployed intelligent systems — plus cross-platform mobile and cloud-native infrastructure.
I follow the complete data science pipeline: collecting and processing real-world datasets, training and evaluating ML models using cross-validation, then deploying them as accessible web applications via Flask REST APIs.
Trained EfficientNet for visual disease classification and multilingual BERT for symptom-based NLP diagnosis. Hands-on experience with deep learning architectures for real-world agricultural applications.
Containerise ML applications with Docker, build Flask REST APIs serving real-time predictions, and deploy on Kubernetes (Azure) or Vercel/Heroku/Render. Full DevOps pipeline from training to production.
Built two full cross-platform Flutter apps shipped to production — a feature-complete e-commerce platform and a social media aggregator. Expertise in Firebase Auth, Firestore real-time databases, and custom data modelling.