03Databricks MLOps for Fraud Detection (Production ML Pipeline)
Role: MLOps Solution Engineer
Executive summary
Built an end-to-end MLOps pipeline on Azure Databricks to productionize a PyTorch fraud-detection model with Feature Store, real-time serving and drift monitoring—cutting deployment time from months to weeks.
- Azure Databricks
- PyTorch
- MLflow
- Feature Store
- Model Serving
- Spark Structured Streaming
- Azure DevOps
A payments provider needed to deploy a PyTorch fraud-detection model in production on Azure Databricks, integrated with a Feature Store, real-time inference and continuous monitoring. Their existing ML process was ad-hoc and lacked an MLOps framework, making deployment and monitoring difficult.
- Refactor the PyTorch model to run efficiently as a distributed Spark job.
- Set up Databricks Feature Store for consistent training/inference features.
- Automate training and evaluation with Databricks Jobs and MLflow, tracking model versions.
- Deploy via Databricks Model Serving for secure real-time scoring.
- Implement monitoring and drift detection (e.g., population stability index on features and scores).
Working with the client's data scientists, I productionized notebook code with MLflow for versioned tracking, populated Feature Store tables (average transaction size, device frequency) updated daily, and configured Databricks Repos with Azure DevOps CI/CD for automated testing. I containerized the model behind a real-time endpoint and built a Spark Structured Streaming job that logs predictions and alerts on significant drift. I aligned ops staff on incident response and documented the pipeline.
The client moved from experiment to a production-grade fraud service with sub-second latency that scales on autoscaling clusters. Every model version is tracked in MLflow with audit trails. Within the first month, drift metrics flagged a behavior shift, prompting proactive retraining. Deployment time dropped from months to weeks with strong reproducibility and monitoring.
MLOps best practices (CI/CD, Feature Store, model registry) are transformative for enterprise AI. A feedback loop with drift detection and human review is vital for fraud, where tactics evolve. Treating PyTorch and Spark as one unified platform achieved scale without rewrites.