AI Engineering
Practical AI projects, experiments, tutorials, and applied research.
Build an AI-Powered Financial Digital Twin: Simulate a $100 Billion Bank with Python
What happens when a bank’s cloud region fails?
It may not remain an IT problem.
Cloud failure → payment disruption → customer impact → deposit withdrawals → liquidity stress.
I explored how Python, NetworkX, SimPy, Monte Carlo simulation, financial…
Build an AI Signature Verification and Fraud Detection System
What if a signature matches perfectly because a fraudster copied the genuine signature?
That’s why signature verification needs more than one neural network.
This project combines:
YOLO detection → Siamese embeddings → structural analysis → document forensics → evidence…
AI Signature Extraction and Manual-Signing Indicator Analysis
Can AI tell whether a document contains a handwritten signature?
Finding the signature is only the beginning.
In this project, we combine YOLO, Segment Anything, OpenCV, feature extraction, quality gates, and human review to build an AI-assisted signature…
Build an AI Deepfake Image Detection System: A Beginner’s Guide to Multi-Evidence Digital Forensics
Learn how to build an AI-powered deepfake image detection system that combines computer vision, image forensics, metadata analysis, and explainable AI to detect manipulated images with confidence.
Build an AI-Powered Market Risk Control Cockpit with Python and Generative AI
Banks process enormous volumes of market data every day—but one stale or incorrect price can affect valuations, P&L, risk metrics, and regulatory reporting.
In this hands-on Python project, we build an AI-powered Market Risk Control Cockpit that…
Build a Face Recognition System with Python and InsightFace
Can AI tell whether two photos belong to the same person?
In this week’s hands-on AI project, you’ll build a complete face recognition system using Python and InsightFace. Learn how AI detects faces, generates facial embeddings, compares…
AI Engineering brings together practical AI projects, experiments, tutorials, and applied research notes on building real-world AI systems. The goal is to make AI engineering easier to understand through concrete examples, beginner-friendly explanations, and practical implementation notes.