Synthetic Data Generation for Healthcare: CTGAN, TVAE, and When to Use Each
When you can’t share real patient data for model development, synthetic data is the answer - if it’s done right. This post covers CTGAN, TVAE, and how to eva...
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machine-learning
Feature Engineering Pipelines: Building Preprocessing That Can’t Leak
Scaling your test data with statistics from test data is a bug. So is fitting your imputer on training + test combined. sklearn Pipelines prevent both - here...
Class Imbalance in Clinical ML: SMOTE, ADASYN, and Why Accuracy Is Lying to You
A model that predicts ‘no readmission’ for every patient can claim 88% accuracy. Here’s how SMOTE, ADASYN, BorderlineSMOTE, and cost-sensitive learning actua...
SHAP From Scratch: From Feature Importance to Per-Patient Explanations
feature_importances_ tells you which features the model used most globally. SHAP tells you why the model made a specific prediction for a specific patient. H...
CatBoost: How It Handles Categorical Features Without Leaking the Answer
The standard way to encode categorical features (target encoding) secretly leaks information from the labels into the training data. CatBoost fixes this with...
LightGBM: Why It’s Faster Than XGBoost and When That Matters
LightGBM trains 10–20x faster than XGBoost on large datasets. This post explains the three algorithmic tricks behind that speed - GOSS, EFB, and leaf-wise gr...
XGBoost Hyperparameters Explained: What Each Knob Actually Does
n_estimators, max_depth, subsample, lambda, gamma - not as a grid to search, but as levers with specific, observable effects. Run each experiment and watch t...
Understanding Gradient Boosting Internals: From Pseudo-Residuals to XGBoost
Most people use XGBoost without understanding what it’s actually computing. This post walks through the internals - pseudo-residuals, learning rate, regulari...
devops
Deploying Two AI Applications on AWS ECS Fargate: A Journey from Local Dev to Production
A detailed, hands-on walkthrough of deploying two LLM-powered applications (EvalAgent Studio and AI Test Driven) on AWS ECS Fargate, detailing architectural ...
Deploying 5 Microservices to AWS ECS - A Complete Hands-On Journey from GitHub to Production
A complete, hands-on walkthrough of deploying five FastAPI microservices and a React frontend to AWS ECS Fargate using CodeBuild and CodePipeline, with path-...
From GitHub Pages to a Real Domain - How I Finally Claimed edwardpraveen.com
A step-by-step account of moving my technical blog off a github.io subdomain, the architecture decisions I weighed, and exactly what I clicked (and skipped) ...
infrastructure
Deploying Two AI Applications on AWS ECS Fargate: A Journey from Local Dev to Production
A detailed, hands-on walkthrough of deploying two LLM-powered applications (EvalAgent Studio and AI Test Driven) on AWS ECS Fargate, detailing architectural ...
Deploying 5 Microservices to AWS ECS - A Complete Hands-On Journey from GitHub to Production
A complete, hands-on walkthrough of deploying five FastAPI microservices and a React frontend to AWS ECS Fargate using CodeBuild and CodePipeline, with path-...
From GitHub Pages to a Real Domain - How I Finally Claimed edwardpraveen.com
A step-by-step account of moving my technical blog off a github.io subdomain, the architecture decisions I weighed, and exactly what I clicked (and skipped) ...