The challenge. The approach.
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.
My scope connected the data and operational layers: AWS Kinesis for ingestion, Lambda processing, DynamoDB storage, feature extraction, and containerized model inference. Predictions feed an alerting path so the output can support maintenance decisions rather than remain an isolated model score.
The MLOps layer includes monitoring, drift detection, and continuous retraining. These mechanisms make changes in incoming data visible and support model updates as operating conditions evolve. The published accuracy is a project-reported result, not a guarantee for another plant or operating environment.
Inside the system.
Streaming feature pipelines
High-frequency sensor inputs flow through AWS ingestion, storage, and feature extraction.
Operational inference
Containerized model serving connects predictions to an early-warning alerting path.
Drift-aware MLOps
Monitoring, drift detection, and continuous retraining support the production model lifecycle.
From input to impact.
Data flow: sensors → IoT Core → Kinesis → Lambda → DynamoDB; then feature pipeline → model → alerts.
The technical foundation.
Technologies reflect the original project. Outcomes are reported from the existing portfolio; client-identifying details are withheld.
Where I would explore next.
These are potential research and engineering extensions, not methods claimed as implemented in this project or additional measured outcomes.
Time-series foundation models
Benchmark pretrained forecasting models against established sensor-specific baselines.
Uncertainty-aware alerts
Explore calibrated prediction intervals and thresholds tied to the cost of missed failures and false alarms.
Asset relationship graphs
Investigate equipment dependencies as additional context for diagnosing patterns across connected assets.