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IoTMLOpsData EngineeringManufacturing
Scalable AI Platform on IoT Basis
Unifying machine data for accelerated ML development
Role:Lead Data Scientist & AI Architect
Duration:10 months
>50%
Faster model development
10x
More data available for training
Unified
Platform across all facilities
The Context
A manufacturing company had IoT sensors across dozens of machines but struggled to leverage this data for AI applications. Each machine type had different data formats, and there was no unified way to train or deploy ML models.
The Challenge
- 1Fragmented data from various machine types and vendors
- 2No standardized pipeline for ML model development
- 3Long cycle times from data to deployed model
- 4Difficulty scaling successful models across facilities
The Approach
- 1Created a unified data lake architecture for all IoT streams
- 2Built standardized feature engineering pipelines
- 3Implemented MLOps infrastructure for model training and deployment
- 4Designed a reusable model template system
Technologies Used
PythonKubernetesMLflowApache Kafka
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