MLOps Architecture with MLflow
Project Information:
I designed and implemented a full MLOps architecture using MLflow (completely open-source) to support production machine-learning workflows for both a large banking institution and a telecommunications company.
What I built:
- Set up MLflow Tracking to log runs, parameters, metrics, and artifacts for forecasting, churn, and risk models—so data scientists and analysts could see exactly what was trained, how, and why it performed the way it did.
- Model registry & promotion workflow
- Designed a model registry process where models move from Staging → Pre-Prod → Production, with automatic versioning, approvals, and rollback paths so that only validated models ever reach customers or financial decision engines.
- Reusable, automated pipelines
- Integrated MLflow with Python-based ETL and training scripts so daily and weekly jobs could run on schedule, pull fresh data, retrain, and compare against existing production models—all without manual intervention.
- Architected the system to run on standard infrastructure (Docker + Python), making it easy to deploy in on-prem, cloud, or hybrid environments used by banking and telecom teams, or any other industry, while staying fully open-source and license-free.
- Monitoring, drift checks, and retraining triggers
- Added hooks to log production performance, detect data drift or degradation in key metrics, and trigger alerts or retraining workflows when models began to “age out” or the underlying customer behavior shifted.
- Documented and standardized the MLOps process so that data science, engineering, and business stakeholders shared a single source of truth for model performance and deployment status.
All of it is powered by MLflow and Python, with a focus on reliability, transparency, and cost-effective, open-source tooling—exactly the kind of MLOps foundation CodeCurrent LLC brings to every client.
