Blog
Latest articles
9 min readGPT Architecture Explained: Beyond the Surface Level
GPT's autoregressive, decoder-only design enables text generation at scale. Here is how it actually works -- from pretraining data to emergent capabilities to GPT-4o.
9 min readAuditing ML Models for Bias: A Practical Guide
ML bias is systematic, measurable, and addressable. This guide covers the types of bias, fairness metrics, audit process, and tools to find and fix disparate model performance.
9 min readKnowledge Distillation: Training Small Models to Match Large Ones
Knowledge distillation lets you deploy fast, small models that match the performance of large ones. Here is how it works, why soft targets help, and when to use it in production.
9 min readHyperparameter Tuning: Finding the Model Settings That Actually Matter
Learning rate, batch size, regularization -- the right hyperparameters can mean 10+ percentage points of accuracy. Here is how to find them efficiently without exhaustive search.
6 min readCross-Validation: Reliably Estimating Model Performance on Unseen Data
A single train/test split gives you a noisy estimate of real performance. Cross-validation gives you a reliable one. Here is every variant, when to use each, and the mistakes to avoid.
9 min readEnsemble Methods: Why Combining Models Beats Any Individual Model
Bagging, boosting, and stacking -- ensemble methods consistently win Kaggle competitions and improve production accuracy. Here is how each works and when to use them.
9 min readNatural Language Inference: The Technique Behind Zero-Shot Text Classification
NLI models can classify text into any category without labeled examples. Here is how entailment-based classification works, the best models to use, and real-world limitations.
9 min readGenerative vs Discriminative Models: The Fundamental ML Distinction
Discriminative models learn decision boundaries. Generative models learn data distributions. Understanding this split explains why LLMs can generate text and when each approach wins.
10 min readReducing ML Model Serving Latency for Production
Users abandon features above 300ms. Here is the complete playbook for hitting production latency targets: quantization, batching, caching, hardware selection, and pre-computation.
7 min readML Research Papers Every Practitioner Should Know in 2026
From Attention Is All You Need to DeepSeek V3 — these are the papers that shaped modern ML. How to read them efficiently and what each one actually contributed.
11 min readThe ML Tools Ecosystem in 2026: A Map of What Is Worth Knowing
PyTorch, MLflow, DVC, vLLM, Airflow -- the ML tooling landscape is vast. Here is a curated map of the tools that matter, what each does, and how to choose for startup vs enterprise.
5 min readLangChain Complete Guide 2026: When to Use It and When Not To
LangChain is a powerful LLM framework, but its complexity is frequently criticized. Here is when it genuinely helps and when you should skip it entirely.