ai-ml-data-science

End-to-end ML pipelines combine structured EDA with drift detection, feature engineering using feature stores, LightGBM model training, and comprehensive evaluation with slice analysis. This workflow handles data transformation via SQLMesh, CI/CD automation, continuous training, and monitoring to ensure reproducibility and train-serve parity in production.

Feature engineering with feature stores creates reusable, versioned transformations tracked through data lineage. Drift detection monitors train-serve parity by comparing data distributions and model performance over time, triggering retraining when degradation occurs and maintaining data contracts across environments.

MLOps integration automates CI/CD pipelines, continuous training schedules, and continuous monitoring dashboards. SQLMesh handles versioned SQL transformations with incremental models, while automated drift detection and model cards provide documentation and governance required for production-grade data science workflows.

LightGBM handles model selection and training within the workflow, while SQLMesh manages SQL-based data transformations across staging, intermediate, and mart layers. Both integrate into reproducible pipelines with version control, enabling validated model development paired with robust data engineering.

Production-ready evaluation includes slice analysis across data subgroups, model cards documenting assumptions and limitations, and comprehensive reporting of performance, bias, and drift. This ensures transparency, reproducibility, and compliance with data contracts required for continuous monitoring and governance.

Data lineage traces transformations from raw data through features to model predictions, enabling reproducibility, debugging, and compliance audits. Combined with model cards and automated documentation, it satisfies governance requirements and supports continuous training by clarifying dependencies and data contracts.