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EdgeML (EDGE)

Advanced AI

Lifecycle stages:

Introduction


MLOps journey description: Edge ML focuses on deploying AI models directly on edge devices (IoT devices, mobile phones, embedded systems) rather than centralized cloud servers. This reduces latency and enhances real-time decision-making.

Use case examples: Running models on-site or on-device in environments with low connectivity or real-time requirements.

  • Real-time pressure anomaly detection on rigs
  • Edge-based image classification for hazard detection on offshore platforms
  • Predictive analytics on portable devices for field engineers
  • On-device analytics for pump and compressor efficiency monitoring
  • Vibration or acoustic analysis from edge sensors for mechanical health