Big Challenges in DevOps in 2019

By | April 25, 2021

Big Challenges Facing DevOps In 2019


Business and technology go hand-in-hand. This is one reason many startups center around IT. Such startup operations understand the exponential growth which accompanies IT, and which was noticed as far back as the sixties by Gordon Moore. About every eighteen months, or so it’s presently reckoned, the computational ability of computers doubles on itself.

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Eighteen years ago, in 2001, it would have been hard to imagine a 128-bit next-generation video game on your cell phone. Now you can download Crazy Taxi for free from the app store, and that illustration actually under-represents just how large a shift has taken place in only eighteen years.

For those in DevOps, keeping pace with this rapid trend requires careful strategy and continual forward momentum. Accordingly, if you haven’t gotten a jump on 2019, you’ll want to. Following are several trends in DevOps that offer both exciting new possibilities, and big challenges over the coming months.


More Integral Machine Learning (ML)

Machine Learning is a process that’s been talked about for years but is now becoming viable. ML provides more effective automation solutions, and can even yield anticipatory data—provided the software is properly managed. That’s where DevOps comes in.

There is new ground to be broken in 2019, and that’s always challenging. Additionally, more and more clientele will come to expect ML solutions if you don’t already provide them. Properly designing options to meet the market will require careful design, Application monitoring via the cloud and other means, and the odd gamble every now and again.

The trend is going in this direction, though; so it’s a very good idea to get on board, or at least optimize your present offerings. Presently, the market in this specific tech application is estimated to exceed $39.98 billion by 2025, and it was only at $1.29 billion in 2016.



Integration Of Artificial Intelligence (AI) And ML

Artificial Intelligence will additionally be a considerable factor in 2019. In combination with ML, AI can make many operational difficulties disappear for a wide variety of businesses. As a hypothetical, consider a manufacturing floor where machines can be maintained at their operational best for the longest period of time and properly managed to the decimal point such that longevity is retained.

Imagine AI automatically facilitating updates, and increasing the simplicity of operation. DevOps solutions built around facilitating AI and ML solutions will be profitable, and they will be difficult—because again, there is new ground to be broken.



More Accurate Predictive DevOps, And Associated Demand

Between AI, ML, and automation, clientele will likely expect greater accuracy in applications and software that’s new to the market. Predictive DevOps are gaining a market foothold, and again this is a big challenge that involves necessarily breaking new ground. You’ll need to use all available solutions to monitor what you’ve put out there, and to help you design competitive solutions.

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Keeping Pace With The Market

2019 will likely see predictive DevOps emanating from AI and ML advances facilitating more efficient automated solutions. Also, it’s expected that ML will exponentially increase in terms of market viability over the coming years. To retain viability as a developer, it’s integral you figure out which advances strategically represent your best options for the coming year.


Design operations around making the same kinds of choices in subsequent years. The “goalposts” of technology are in continual forward motion—that’s key to the industry. Balancing your business around this reality will help you to stay continuously viable even in unsure markets.

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