📚 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.

Applied time-series forecasting and anomaly detection using ML and statistical baselines, with rigorous experimentation, residual-driven diagnostics, and reproducible evaluation workflows.

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.

Interpretable credit risk modeling using real-world lending data, with emphasis on probability calibration, decision relevance, and scalable machine learning workflows.

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

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

A conceptual framework for deploying and managing AI/ML services within specialized hardware engineering environments.

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.

End-to-end applied ML system for predicting next-cycle manufacturing cycle times. Includes Spark-based ETL, leakage-safe temporal splits, baseline regressors, LSTM sequence modeling, and SHAP-based explainability under real operational constraints.

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

PersonaTTS is a personalized neural text-to-speech system that learns a user’s vocal persona from a short speech sample and generates natural speech for arbitrary input text.

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

No description provided.

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

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

Hybrid retrieval benchmark for vehicle memory banks with text, metadata, visual features, offline evaluation and FastAPI serving.

B2B SaaS applied ML MVP built with OpenClaw: churn risk scoring + next-best-action copilot (3-min demo available)

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.

Fintech applied ML MVP built with OpenClaw: smart collections prioritization with strategy recommendations (3-min demo available)

Football decision intelligence system that transforms risk and performance models into actionable player management decisions under real-world constraints.

Develop interpretable credit risk models using lending data to improve default probability estimates for sound financial decisions.

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.

Structured implementation of core machine learning algorithms with practical experimentation, evaluation, and real-world problem-solving focus.