DevOps Produces Deeper Solution for Deep Learning

By CIOReview | Friday, June 21, 2019

DevOps has already conquered the technology sphere. DevOps, infused with deep learning technology is about to give rise to DeepOps to take the technology market to its newest pedestal.

FREMONT, CA: Before DevOps was a widespread practice, the world of operations and development existed in a constant state of struggle. Developers were instructed to think in new ways, whereas operations staff were tasked with keeping all systems stable. From those irritations, a culture of DevOps was born, closing the gap between development and operations to enable enterprises to carry services and applications faster and more proficiently than ever before. Engineers now operate across the entire development lifecycle. Increased collaboration between engineers and other teams has led to a more streamlined development procedure. The fundamental of DevOps, which includes running tests and tracking changes through automated processes, has completely altered how software development functions in a brief time.

Why Deep Learning is Crucial to DeepOps:

At present, Deep learning is one of the most technically advanced fields in the technology sector, and has even been referred to as “Software 2.0.” It powers everything from social network algorithms and voice assistants to automated medical devices. But the development of DevOps is being stalled by inefficient and outdated apparatuses and practices that hold it back from reaching its full potential.

Enter of DeepOps:

DeepOps today is where DevOps was decades ago—a promising field that is becoming increasingly essential. It begins with simple but essential characteristics like addressing key points in the deep learning development lifecycle.

Data scientists have conducted thousands of experiments on a large amount of data, different codes, and dissimilar computing environments to make sure that they are working toward the correct solution. But with the sheer amount of files and their inflated sizes, it is challenging to handle without a DeepOps arrangement in place. DeepOps will assist in shortening some of the major pain points in ML advancement procedures if put into practice. DeepOps will also save, segment, and process large data sets, and help new data scientists to pick up difficult tasks and venture into uncharted areas of technological expansion.