Industrial internet of things (IIoT) is already making its presence felt in domains such as automobiles, healthcare, and manufacturing. But the actual worth of the IIoT will be accomplished only when the sensor data will be backed by Machine Learning (ML).
Enterprise IoT and connected devices have been chiefly enabled by cloud computing. Cheaper storage coupled with abundant computing power is the crucial driving factor behind the growth of IIoT. Even though it was probable to gather data form numerous devices and sensors, consumers found it extensively costly to maintain huge datasets. Even after ample resources were allocated for storages, the horse power required to computing the process query and assess these datasets was absent in the data center enterprise. Most of the resources available were distributed to business intelligence systems and data warehouses that are important to businesses. The adoption of cloud as an additional data center alter the industry equation. Industry verticals such as automobiles, aviation, healthcare, and manufacturing are now gathering every probable data point that is generated by the sensors. They are making full use of the Big Data, cloud storage, and Big Computing capabilities that are given out by the big public cloud providers. This has turned out to be the single most crucial aspect in catalyzing the adoption of IIoT in industrial enterprises.
One of the important areas of Machine Learning is the figuring out patterns form the available dataset to assemble same data points and project the future data points’ value. Advanced algorithms associated to both unsupervised and supervised ML can be utilized for predictive and classification analytics. Since these programs can grasp form the existing data, they can single out baseline openings without defining them explicitly. These algorithms can project future sensor values based on the previous data as majority of IoT data is built on time-series.