@hemnam

Building Generative AI Applications the Right Way

17h ago · 4 min read · Generative AI is more than just prompting a model and getting responses. In real applications, it follows a structured lifecycle—from defining a clear use case to deploying and continuously improving

Why Machine Learning Models Break After Deployment

3d ago · 6 min read · Building a machine learning model is only the beginning. What truly determines success is how that model is deployed, monitored, updated, and maintained in production. Today, I learned about MLOps (Ma

Evaluating ML Models Like a QA Engineer (Not a Data Scientist)

6d ago · 4 min read · One of the most important lessons I learned today is that building a machine learning model is only half the job. The real challenge is knowing how well the model performs, whether it generalizes, and

Breaking Things (and Building Better Tests): My Hackathon Experience

Apr 16 · 5 min read · I recently participated in the Breaking Things Hackathon hosted by Hashnode and sponsored by Bug0, and I can confidently say—it was one of the most fun and exciting hackathons I’ve joined. What made t

The Machine Learning Development Lifecycle (And Why QA Is Critical at Every Stage)

Apr 15 · 4 min read · Most people talk about machine learning as models and algorithms.Today I learned something far more important: ML systems fail not because models are weak — but because quality is ignored across the