I architect and deploy end-to-end AI systems — from data ingestion and model training to optimized inference pipelines and scalable backend deployment.
- ⚙ Real-time detection & tracking systems
- 🧠 Deep Learning inference optimization
- 🚀 ML-powered REST architectures
- 🏗 Scalable AI backend infrastructure
Intelligence is useless until it’s deployed.
- Supervised & Unsupervised Learning
- Feature Engineering
- Model Evaluation & Benchmarking
- Hyperparameter Optimization
- Production Model Deployment
- YOLO-based Object Detection
- Multi-Object Tracking (SORT)
- OCR Pipelines
- Face & Hand Gesture Recognition
- Real-Time Video Processing
- Flask REST APIs
- PostgreSQL Architecture
- Model Serving Pipelines
- Scalable Python Systems
- Modular Codebase Design
NumPy • Pandas • Scikit-learn • XGBoost • MediaPipe • Seaborn
Stack: Python • OpenCV • MediaPipe • TensorFlow • PyAutoGUI
- Real-time landmark detection
- Gesture-driven automation interface
- Low-latency inference optimization
- Efficient video frame pipeline
🔗 https://github.com/shazimjaved/Face_hand_gesture_recognition
Stack: Python • YOLOv8 • OpenCV • SORT • OCR
- YOLOv8 detection engine
- SORT tracking integration
- OCR extraction pipeline
- Real-time vehicle monitoring system
🔗 https://github.com/shazimjaved/License-plate-recognition
Stack: Flask • XGBoost • PostgreSQL • Gemini API • Seaborn
- ML-based risk prediction model
- RESTful backend architecture
- Structured relational database
- AI-assisted medical insights
🔗 https://github.com/shazimjaved/Advanced-cardiovascular-system
- Systems > Scripts
- Deployment > Demos
- Optimization > Assumptions
- Architecture > Chaos
- Impact > Hype
