Why You Need a Modern Infrastructure to Accelerate AI software and ML Workloads

The current era is the age of Big Data and all technologies related to the generation and processing of this data.

With the exponential growth in data generation from myriad sources like our smartphones and network-connected devices namely Internet of Things, the opportunities held in it are also infinite. And every organization today aims to tap this enormous source of data with the hope of gaining insights that can boost their business.

However, managing this data is neither easy nor simple, because of its sheer volume. It becomes particularly difficult when legacy AI software is used to execute complex logical deductions on such massive volumes of data.

Why use modern AI software instead of legacy infrastructure?

To answer this question, the result of a Forbes’ survey must be mentioned here, that as of the year 2016, 90% of the total world data was created in just 2 years. And if you are to believe estimates, then given the current pace, each day almost “2.5 quintillion bytes of data” are being generated! (Source)

This figure is indeed hard to conceptualize, and not to mention the considerable percentage of unstructured data that is also contained. According to various sources, Gartner has reported that organizations have experienced almost 50% growth for unstructured data every year. (Source)

Unstructured data is equally vital in providing valuable market insights to propel businesses. But this boom has put forth certain challenges when it comes to managing such high volumes of unstructured data. The primary reason is the difficulty in scaling up the existing software infrastructure. The rate of data growth has already fast surpassed the speed in which legacy software can be possibly scaled up to accommodate the sheer volumes of unstructured data. This applies to both Artificial Intelligence as well as Machine Learning software.

Put in simpler words, this increasing volume of data is an unanticipated situation, which legacy software is not designed to handle. Plus, there is an increasing sense of urgency for the enterprises to extract insights before the data turns stale. Only modern AI & ML solutions that can be scaled up easily can manage this unprecedented growth of unstructured data and service needs.

What are the benefits of using modern infrastructure?

Modern data analytics infrastructure has revolutionized how unstructured data can be stored and processed by organizations.

New-age solutions for data workloads especially for neural networks and deep learning are designed to leverage the benefits of networks that offer high bandwidth. These can carry out parallel processing of data that is both structured and unstructured, and streamline the feed of workloads of ML software. So it effectively eliminates any input-output bottleneck and automatically boosts the performance profile of modern AI software.

And with the removal of performance bottlenecks, it also optimizes the company’s investments in setting up modern IT infrastructure.

It is expected that by investing in modern IT infrastructure for AI and ML workloads, enterprises can further boost their revenues and make effective use of high-performing resources.