Industrial IoT Edge Architecture for Machine and Deep Learning
ecosystem including device abstraction to transport data to VMware Pulse IoT Center™ , which offers an end to end infrastructure management solution for secure and reliable data flow from devices to applications . The figure below shows the different ways that Pulse IoT Center connects with edge devices . See details at : www . vmware . com / products / pulse . html . Liota is part of Pulse IoT Center ; however , it can be used separately to orchestrate data from sensors through gateways to other IoT services .
Figure 12 : Platform Tier Architecture for Deep Learning
PLATFORM TIER ARCHITECTURE FOR DEEP LEARNING
The Platform tier receives real-time and batch data from the edge and performs Deep Learning model inference and training . It caches raw data as well as processed data after feature engineering . It also transfers the final model inference results to the enterprise tier for Business Intelligence ( BI ) reporting and visualization .
The Platform tier performs Deep Learning training as well as inference . The platform pulls raw data from the edge cache , as well as performs feature engineering on real-time and stored edge data . The feature data includes information from all edges to create aggregate information such as median temperature of devices at all edges .
Use Case for Edge and Platform Tier Learning
The example below shows a use case for separating Machine and Deep Learning at the edge and Platform tiers .
Figure 6 shows temperatures from four devices . The median temperature of all four devices ( in red ) is overlaid on the device temperatures in blue . The edge can detect the anomaly in Device 3 due to a sudden drop in temperature but the anomaly in Device 4 is
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