About
A mid-size manufacturer with 200+ CNC machines faced $4M+ annual losses from unplanned failures. UppLabs built an ML platform ingesting real-time data from 2,000+ IoT sensors using LSTM autoencoders and GANs for synthetic data augmentation. Predictions delivered 2 to 3 weeks in advance via Apache Kafka and Flink streaming pipeline. Result: 73% less downtime, 89% accuracy, $3.2M savings in 8 months.
Challenge, approach, and impact
Noisy IoT Sensor Data at Scale
2,000+ sensors generating 50GB of time-series data daily with significant noise, missing values, and sensor drift. Extracting meaningful failure signals from this volume required robust preprocessing and domain-specific feature engineering before any ML modeling could begin.
Extreme Class Imbalance in Failure Data
Equipment failures were rare events against a sea of normal operation data. A class imbalance ratio of 1 to 10,000 made standard ML approaches ineffective, requiring specialized augmentation techniques to train accurate prediction models.
Real-Time Processing Across 200+ Machines
Predictions needed to update every 5 minutes across 200+ machines simultaneously while maintaining sub-second dashboard response times. This required a streaming architecture capable of real-time feature computation at scale.
Low Technician Trust in AI Predictions
The maintenance team was skeptical of AI-generated predictions. The system had to earn trust through explainable outputs with clear evidence, confidence scores, and a gradual rollout strategy before full adoption could be achieved.
How we built
Testimonials
John C.
Confidential
“UppLabs built a predictive maintenance platform that transformed how we operate our manufacturing floor. Before the engagement we were losing over $4M annually to unplanned equipment failures. Within 8 months the system was predicting failures 2 to 3 weeks in advance with 89% accuracy. Downtime dropped by 73% and we recovered $3.2M in annual savings. Their team took full delivery ownership from ML architecture to production deployment with zero management overhead on our side.“
Team structure
Client team
C С
Head of Operations
Project stakeholder
С С
Chief Technology Officer
Project stakeholder
The client stakeholders were working closely with the team at UppLabs
Agency team
2 x ML Engineer
Governance
2 x Backend Developer
Production
1 x Frontend Developer
Governance
1 x QA Engineer
Governance