AI will further accelerate the deployment of Data Centers and many of them will be close to your company.
We are currently experiencing a bombardment of examples of AI summarizing documentation in record time, writing articles or providing opinions on specific issues. All these tasks have in common that the source of the information on which the AI relies is processed information. But it won’t be long before AI will also be analyzing data, interpreting it and advising us. AI is but one piece in the ecosystem needed for this revolution. The other 3 pieces are sensorization, the communications network and data centers, but could the latter become saturated with the massive influx of data and the growing demand for computing?
AI will further accelerate the deployment of Data Centers and many of them will be close to your company.
We are currently experiencing a bombardment of examples of AI summarizing documentation for us in record time, writing articles for us (note: this article was written by humans) or giving us opinions on specific issues. All these tasks have in common that the source of the information on which the AI relies is elaborated information such as manuals, articles or prose content in general. But it won’t be long before AI will also be analyzing data, interpreting it and advising us. AI is only one piece in the ecosystem needed for this revolution. The other 3 pieces are sensorization, the communications network and data centers, but could the latter become saturated with the massive influx of data from sensors (such as CPMs or the Nubeprint app) and the growing demand for computation (for example, to give advice on how to improve margins on cost-per-copy contracts)?
AI, big data and sensorization are all part of the equation in an ecosystem that is changing everything. From managing a factory’s packaging plant, to advising a gift store on what products it should buy to cater for Black Friday sales. Sensorization is about having sensors collecting data at all stages of a production process. But the data often needs to be filtered and polished, and once the veracity and accuracy of the data is guaranteed, it is ready to be used by generative AI. That complex task is what Nubeprint has been devoting its efforts to when in 2017 it introduced its Machine Learning engine.
As the amount of data being collected with sensors grows (the number of printers monitored with the Nubeprint app from the user’s cell phone has grown by 750% so far this year), so have storage and processing needs : this takes place in data centers. If the entire system is structured with a few large data centers, sooner or later there will be a bottleneck in networks and computing resources. For this reason, a more recent trend has been to opt for an IT architecture with distributed computing capabilities in which data centers close to the source (known as edge data centers) coexist with large or giant data centers (located in strategic places where, once the problem of how the data arrives has been solved, the cost of land and, above all, energy is a premium). In this way, data that require immediate processing are treated in Edge Data Centers, while historical data are sent to the larger Data Centers, traveling even thousands of kms. And it is in the latter that tasks that do not require immediacy are executed.
Nubeprint offers a managed MPS solution with dynamic algorithms and filters. In 2013, it developed the first A.I. engine for MPS and, since 2017, it has a Machine Learning (ML) developed specifically for MPS: through this machine learning, the system develops pattern recognition and the ability to learn continuously, with predictions based on big data, after which it makes the necessary adjustments without having been specifically programmed to do so.
