Writing about AI architecture, deployment challenges, and lessons learned from the trenches of software engineering.
How I built an end-to-end football prediction ML engine using Poisson Regression, XGBoost, time-aware feature engineering, exact-score probability modeling, formation prediction, and starting XI analysis.
The model is the easy part. Here is the full system architecture — async job queues, bounded worker pools, load shedding and multi-key rotation — that let a Google Gemini photobooth survive 30,000 people uploading selfies at a live football event.
RAG systems are only as good as their retrieval architecture. Here's how I build pipelines that stay accurate at scale...
Fine-tuning transformer models for Bangla taught me more about NLP fundamentals than any course. Here's what worked...
Most AI projects fail not because of bad models, but because of terrible deployment practices. Here's the infrastructure checklist I use...
Most RAG tutorials show you the happy path. Here's what I learned building a retrieval-augmented generation system that serves thousands of queries daily — including the parts nobody talks about.
I designed, built, and shipped a full Uber-like luxury transportation platform — passenger app, driver app, and super admin panel — entirely solo. Here's the full technical breakdown of TriState Ride.