Client
Industrial IoT SaaS for Factory Operations Manufacturing / Industrial IoT / Predictive Maintenance
Challenge
Project Overview : The client managed connected factory equipment across multiple facilities and was facing unexpected downtime due to equipment failure. I implemented a predictive maintenance system that monitored sensor data and forecasted breakdowns before they occurred.
Solution
My Role : Built ML pipelines to analyze machine telemetry, deployed predictive alert models, and created a lightweight real-time API to integrate with their factory dashboard. Tech Stack : Python (Scikit-learn, TensorFlow), Kafka, PostgreSQL, Flask
Results Achieved
40% reduction in unplanned downtime, 30% decrease in maintenance overhead, Increased equipment availability and factory output.
Client Feedback
We used to react to failures. Now we proactively schedule fixes and keep machines humming — this made a huge difference operationally.