Industrial IoT Edge Architecture for Machine and Deep Learning
events are going to be routine without any anomaly.
Figure 2 shows cost of compute vs data transfer cost benefit of edge-based ML / DL applications.
Learning consists of the following types:
1. Unsupervised where we do not require training data and assume that normal instances are far more frequent than anomalies.
Figure 9: Cost of compute vs. data transfer cost benefit of edge-based ML / DL applications
In the following sections, we shall describe the following – a short summary of Deep Learning, edge tier innovations for DL, Platform tier innovations for DL and a use case of sensor based trucking.
WHY MACHINE AND DEEP LEARNING
The science and practice of artificial intelligence is vast and consists of the following types:( 1) Data,( 2) Learning and( 3) Algorithms. Data consists of:
1. Structured data such as time series data, events and graphs and
2. Unstructured data such as video, images, speech and text.
2. Semi-Supervised where training data has labeled instances of only one class. We also assume that normal instances are more frequent than anomaly classes.
3. Supervised where training data has labeled instances for normal and anomaly classes. We also assume accurate representation of the labels for the classes.
4. Reinforcement where rewards are provided to guide the learning through policy.
We next consider the types of algorithms. Here we diverge into Machine Learning algorithms which consist of anomaly
IIC Journal of Innovation- 61-