About the service
I turn your data into working predictions. I develop machine learning models for forecasting, classification, regression, and recommendations and handle the entire path: from raw data export to a service that can be called by your product. First I understand the data and the task, clean what needs cleaning, handle missing values and outliers, and construct features that actually carry signal, not noise. Then I train and compare several model families, honestly tune hyperparameters, and pick the one that wins on your real metrics, not on a pretty table.
I don't cut corners on quality assessment. I validate the model on held-out and time-aware splits so the numbers you see match what will happen in production. I report accuracy, recall, confidence intervals for error, and calibration in plain language, not just a single number. I look for leaks, watch for data drift, and check if the model is quietly flattering itself on training data. You get a clear picture: where the model is strong, where it's weak, and how confident it is in each prediction, so decisions are made with open eyes.
When the model proves itself, I package it in a clean documented API that can be called from anywhere, with versioning, monitoring points, and a retraining procedure so accuracy doesn't slowly drop unnoticed for months. I explain how everything works, hand over the training code, and stay available while your team learns. Whether you need demand forecasting, churn estimation, ticket ranking, fraud signals, or personalized recommendations — I give you a model that's measured, ready for deployment, and stays yours.
— Data cleaning, feature engineering, honest evaluation
— Forecasting, classification, regression, recommendations
— Deployment as a versioned API with retraining
I don't cut corners on quality assessment. I validate the model on held-out and time-aware splits so the numbers you see match what will happen in production. I report accuracy, recall, confidence intervals for error, and calibration in plain language, not just a single number. I look for leaks, watch for data drift, and check if the model is quietly flattering itself on training data. You get a clear picture: where the model is strong, where it's weak, and how confident it is in each prediction, so decisions are made with open eyes.
When the model proves itself, I package it in a clean documented API that can be called from anywhere, with versioning, monitoring points, and a retraining procedure so accuracy doesn't slowly drop unnoticed for months. I explain how everything works, hand over the training code, and stay available while your team learns. Whether you need demand forecasting, churn estimation, ticket ranking, fraud signals, or personalized recommendations — I give you a model that's measured, ready for deployment, and stays yours.
— Data cleaning, feature engineering, honest evaluation
— Forecasting, classification, regression, recommendations
— Deployment as a versioned API with retraining
