The IoT in 2018: Four reasons to excited, four reasons to be worried
IoT big data analysis is becoming more accessible
In many respects, hardware devices and cloud platforms serve as the base foundation for data generation, transport, and storage. Turning data streams into useful information requires appropriate software tools, and IoT developers can find a broad array of offerings from IoT service providers. Tools range from display panels showing streams of data much like an oscilloscope signal trace to more sophisticated analytic suites.
One of the key value propositions of the IoT is to provide streams of data from which enterprises can identify key events and take appropriate action -- preferably with minimal latency between data generation and identification. Indeed, developers can take advantage of a growing array of tools for big data designed for this purpose.
Among those tools, machine learning tools have evolved rapidly and promise to improve the ability to recognize patterns in complex data sets. In fact, IoT developers can begin to move ML-based recognition filters to edge devices. Google's TensorFlow Lite, a compact version of its TensorFlow machine learning tool, allows developers to generate models able to run successfully on resource-constrained devices (Figure 6).
Figure 6. Google’s TensorFlow Lite architecture provides a path for machine-learning model deployment on mobile devices and small embedded systems. (Source: Google)