Coforge's Data Cosmos-ML-Flow

Cosmos-ML-Flow is Coforge's production-grade MLOps accelerator built on Databricks that automates the complete ML lifecycle — from feature engineering and model training to batch inference, continuous monitoring, and interactive dashboards. Part of Coforge Data Cosmos™ — the innovation backbone combining platforms, agentic accelerators, and services for end-to-end data engineering, BI, governance, and analytics — it delivers a config-driven, reusable framework for deploying and governing ML models at scale with Unity Catalog.

The platform automates the end-to-end ML lifecycle through key capabilities: • Automated Feature Engineering – 10+ behavioral signals: temporal patterns, user velocity, transaction sequencing • Dual Model Training – LightGBM (Champion) + XGBoost (Challenger) with Hyperopt tuning (TPE, 50 trials) • MLflow Experiment Tracking – full lineage of parameters, metrics, artifacts, and feature importance • Automated Quality Gates – 5-point validation (accuracy, F1, precision, recall, AUC-ROC) before production • Unity Catalog Model Registry – Champion/Archived alias management with versioning and audit trails • Batch Inference at Scale – Spark-distributed scoring handling millions of transactions per cycle • Continuous Monitoring – PSI/KS drift detection across numeric features with automated alerting • Interactive Dashboard – 5-page Streamlit UI for executive, fraud, performance, drift, and scoring views

Three automated pipelines — Training, Inference, and Monitoring — run on a YAML-based config-driven architecture requiring no code changes for threshold, schedule, or parameter adjustments.

Key Use Cases: • Banking – transaction fraud detection with dual-model approach and Unity Catalog lineage for compliance • Insurance – claims fraud scoring with quality gates ensuring actuarial-grade precision • Travel – revenue optimization and dynamic pricing with continuous drift monitoring • Healthcare – clinical risk scoring with HIPAA-compliant governance through Unity Catalog

The 8-week implementation engagement covers: discovery and use-case scoping, platform deployment on Azure Databricks, feature engineering setup, dual-model training configuration, quality gates and model registry setup, batch inference enablement, drift monitoring configuration, dashboard setup, and knowledge transfer.

Target Audience: Data Science & ML Engineering Teams, MLOps Engineers, Data Engineering & Platform Teams, Data Architects, and Analytics & Risk Leaders.