Case Study

A manufacturer's predictive maintenance models were built by a data science team but never reached production — no deployment pipeline, no monitoring, no path to retraining.

Built the MLOps layer the models were missing: containerized model serving, drift monitoring, and an automated retraining pipeline tied to sensor data ingestion.

• IoT sensor ingestion via Kafka
• Model registry and versioning with MLflow
• Kubernetes-based model serving
• Automated drift detection triggering retraining pipelines