Custom ML Solution Development

Custom ML Solution Development

Machine Learning solves tasks of prediction, classification, clustering, and optimization — where algorithmic rules fail due to high dimensionality or nonlinearity. We develop production ML systems with emphasis on reproducibility, monitoring, and long-term support.

Problem Classes

Supervised Learning:

• Binary and multi-class classification: fraud detection, churn prediction, disease screening, sentiment analysis
• Regression: price forecasting, demand prediction, KPI estimation
• Structured data — XGBoost, LightGBM, CatBoost; unstructured data — transformers, CNN

Unsupervised / Self-supervised:

• Customer clustering (K-Means, DBSCAN, GMM)
• Anomaly detection (Isolation Forest, AutoEncoder, One-Class SVM)
• Representation learning for downstream tasks

Ranking and Recommendations:

• LTR (Learning to Rank) for search
• Collaborative / Content-based filtering
• Multi-armed bandit for real-time optimization

Tabular Data: Not every task requires neural networks. For structured data with hundreds of features, gradient boosting often outperforms neural networks with significantly lower data and computational requirements.

Critically Important Stages

Data Analysis: EDA is not a formality. Before modeling: distributions, correlations, missing values patterns, target leakage check. Poor data analysis = beautiful metrics on test set and failure in production.

Feature Engineering: For tabular tasks — the main quality factor. Temporal features, aggregates, lag features, interactions. Automated feature selection (SHAP, permutation importance).

Model Selection and Hyperparameter Tuning: Optuna (TPE sampler) for automatic search. Cross-validation robust to temporal leakage for time-series tasks.

Calibration: For classification tasks — probability calibration (Platt Scaling, Isotonic Regression). Uncalibrated probabilities lead to incorrect business decisions.

MLOps from Day One

Experiments in MLflow with automatic metric logging. Model Registry — staging → production promotion via CI/CD. Feature and target variable drift monitoring (Evidently AI). Automatic alerts on quality degradation.

Delivery

Final artifact — not a Jupyter notebook. This includes: packaged inference service (FastAPI + Docker), tests (unit + integration), API documentation, retraining runbook, monitoring dashboard.