Real-world ML architecture. Actionable code. Zero fluff.
This blog features practical engineering insights drawn from nearly a decade of building and deploying machine learning systems across 10+ industries. From optimizing recommendation engines and NLP pipelines to structuring robust MLOps lifecycles, every post delivers proven design patterns, architectural decisions, and the exact strategies needed to take models from local sandboxes to production scale.
"Algorithms are crude. Computers are machines. Data science is trying to make digital sense of an analog world. And AI is the output."
44 articles published
Hard-won lessons on building data science teams — hiring for potential over pedigree, skill assessment frameworks, mentoring junior practitioners, fostering data culture, and avoiding organizational pitfalls.
An honest account of independent data science consulting — scoping projects, managing client expectations, demos vs production, pricing strategies, and what 'production-ready' actually means.
A practitioner's guide to data science in banking and finance — credit scoring, fraud detection, customer segmentation, explainability, imbalanced data, and temporal leakage pitfalls.
A deep dive into collaborative filtering, content-based approaches, Learning to Rank, real-time serving architectures, and A/B testing frameworks for e-commerce recommendation systems.
A practitioner's guide to probabilistic CLV models (BG/NBD, Gamma-Gamma), regression-based approaches, CLV-based segmentation, and translating CLV insights into marketing budget allocation decisions.
Practical guide to demand forecasting using Prophet, ARIMA, and machine learning approaches for FMCG marketing — handling promotions, seasonality, hierarchical forecasting, and translating forecasts into marketing action.
