📚 Curated collection of engineering blogs detailing real-world applications of LLMs in solving specific business problems.

📚 Curated list of machine learning engineering blogs.

📚 Curated collection of blogs and papers on how different companies are using machine learning in production for better customer support.

This article explores the theory behind explainable car pricing using value decomposition, showing how machine learning models can break a predicted price into intuitive components such as brand premium, age depreciation, mileage influence, condition effects, and transmission or fuel-type adjustments.

Experimental web application demonstrating how an offline-trained financial fraud detection model can be exposed through a web interface. Built with Flask and a pre-trained XGBoost model to showcase ML inference flow, feature engineering, and result communication — not a production fraud prevention system.

Data-driven modelling framework for utility-scale solar PV inverters, covering digital twins, forecasting, anomaly detection, and maintenance analytics.

This project detects people in a video, tracks their movements, counts them as they cross designated lines (IN/OUT), and generates a heatmap showing areas with the most movement.

🚗 Decode car values using a transparent machine learning system that enhances price understanding through explainable methods.

From academic concepts to applied machine learning with ensembles — structured workflow, hyperparameter tuning, and real-world implementation.

Production-style ML model monitoring system with drift detection, delayed labels, retraining and safe promotion.

Applied Data Scientist & Analyst | Python • SQL • ML • Forecasting • Analytics

Applied ML system predicting urban accessibility across Barcelona using geospatial features, SMOTE and Random Forest. Built for inclusive mobility.

Adult Income Drift Lab conducts a comprehensive model stability analysis under demographic covariate shift, combining statistical drift detection with performance and calibration evaluation on real-world census data.

Policy-constrained LoRA fine-tuning to reduce hallucinations in a billing-focused LLM, using a PayFlow (fictional SaaS) use case with before–after evaluation.

Deterministic decision gate for AI/ML systems. Risk-Gate enforces strict, schema-driven admissibility boundaries between AI/LLM intent and real system actions. It provides a fixed, human-owned decision structure with deterministic allow/block outcomes, explicit audit logging, and environment-specific policy via configuration — no ML, no heuristics,

🏙️ Transform urban data into insights with the Barcelona Accessibility Intelligence System, enhancing mobility and inclusive city planning through machine learning.