5. System Architecture¶

Modeling & Feature Engineering Toolkit¶

Tool / Library	Purpose & Highlights
Scikit-learn	Essential Python library for machine learning, supporting a wide range of supervised and unsupervised algorithms, as well as data preprocessing and model evaluation.
Optuna	Automated hyperparameter optimization framework; efficiently searches for optimal configurations through adaptive and distributed experimentation.
TSFresh	Automates the extraction of a large number of time series features, enabling advanced signal processing and feature engineering for temporal data.
SHAP	Delivers model interpretability via Shapley values; quantifies the contribution of each feature to individual predictions for robust explainability and auditing.
HierarchicalForecast	Specialized library for forecasting hierarchical time series, ensuring coherence and consistency across all levels of data aggregation (e.g., national, regional, store, SKU).

MLOps, DataOps & Productionization¶

DVC: Data and pipeline versioning for full reproducibility and collaboration
MLflow: Experiment and model tracking, registry, and deployment
Apache Airflow / Dagster: Workflow orchestration for ETL, training, evaluation, and reporting pipelines
FastAPI: High-performance, production-ready API serving for real-time inference
Streamlit: Business Intelligence dashboards for interactive analytics and visual monitoring
Optuna: Integrated with pipelines for scalable, automated hyperparameter search
Slack (or alert system): Notifications, anomaly detection, and workflow monitoring

Tool / Component	Key Role in Production Workflow
Streamlit	Interactive dashboards for operational monitoring, real-time visualization of trends, model outputs, and error analysis. Connects live to APIs or batch outputs.
DVC	Data and pipeline version control; tracks every change in datasets, model artifacts, and intermediate files. Enables reproducibility and seamless team collaboration.
MLflow	End-to-end ML lifecycle management. Tracks experiments, metrics, hyperparameters, and model versions; supports model registry and REST API/batch serving.
Dagster	Modern orchestrator for building, scheduling, and monitoring complex data and ML workflows. Ensures reliability and observability in production pipelines.
Apache Airflow	Industry-standard DAG-based orchestrator. Automates ETL, model training, evaluation, and scheduled inference. Enables robust error handling and pipeline reusability.

This technology stack ensures high scalability, reliability, and full traceability for all machine learning and data operations—from data ingestion and feature engineering to model deployment and business reporting.