About a decade ago, the software engineering industry reinvented itself with the development and codification of so-called devops practices.

Devops, a compound of “development” and “operations,” refers to a set of core practices and processes that aim to decrease time to market by thoughtfully orchestrating the tight integration between software developers and IT operations, emphasizing reuse, monitoring, and automation.
In the years since its introduction, devops has taken the enterprise software community by storm garnering respect and almost-religious-like reverence from practitioners and devotees.Today, at the dawn of 2018, we are seeing a subtle but profound shift that warrants a reexamination of established software development practices.
In particular, there is a growing emphasis on leveraging data for digital transformation and the creation of disruptive business models concomitant with the growth of data science and machine learning practices in the enterprise.

As adoption of big data computing platforms and commodity storage becomes more widespread, the ability to leverage large data sets for enterprise applications is becoming economically feasible. We’re seeing massive growth in investments in the development of data science applications—including deep learning, machine learning, and artificial intelligence—that involve large volumes of raw training data.

The insights and efficiencies gained through data science are some of the most disruptive of enterprise applications.To read this article in full, please click here

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