Visual data recording and the IoT
Researchers at IBM recently announced that the firm’s distributed visual data recognition models can now be trained faster than competing models from Facebook, according to TechCrunch.
Distributed visual data processing is a subset of deep learning, which is a branch of machine learning, that uses several graphics processing units (GPUs) to process and analyze massive data sets. It’s ideal for very large deep learning projects where the data is too large to be processed on a single GPU.
While not specifically designed for the IoT, distributed visual data recognition could aid larger IoT ecosystems in particular.
And that’s crucial because large IoT projects are beginning to comprise a larger share of the overall IoT. In the past, many companies deployed only a few dozen IoT devices to enhance their operations, but that’s starting to change as firms are scaling up their projects and may soon deploy hundreds of devices — 36% of global IoT projects now involve 100 or more devices. That means these firms will need to process much more data from these devices, which distributed visual data recognition models could help accomplish; this, in turn, could heighten demand for IBM’s models in the coming years. Read more…
