Predictive Maintenance ML Pipeline

Engineered an end-to-end MLOps pipeline for predictive maintenance using high-frequency sensor data from manufacturing plants. The system ingests IoT sensor streams, runs feature extraction and model inference, and triggers alerts when failure is predicted. The architecture uses AWS (Kinesis, Lambda, DynamoDB) for scalable ingestion and storage, with containerized model serving and continuous retraining. Monitoring and drift detection are built in to keep the model reliable in production.