Future of DevOps is AI and machine learning

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Technology in general is about adding convenience and improving quality of life. Automating routine tasks is just common sense. Automation is also a core component of DevOps. The next level of evolution for automation would be for machine learning and artificial intelligence to enable tasks to be automated that are simply impractical to do at all by humans using manual methods:

Automation is the fuel that drives DevOps. Automating routine, repeatable tasks is one of the defining characteristics of DevOps culture. As artificial intelligence (AI) and machine learning improve, the scope and complexity of the tasks that can be automated increases, which raises the bar for all of DevOps. The humans behind the DevOps will be freed from even more mundane tasks and able to focus on more innovative and creative endeavors.

It makes sense, if you think about it. The trend from physical services in an on-premise data center, to virtualization, to cloud services, to DevOps has been a steady march of raising the bar for automation. The more applications and entire infrastructures can be developed, deployed, monitored and managed programmatically and through automation, the more efficiently and effectively an organization can function.

As tasks are offloaded to automation, humans can address other challenges. That focus inevitably leads to identifying other new tasks that can be automated. AI and machine learning represent a quantum leap in this evolution, however—with AI and machine learning, the automation can begin to identify and improve itself. When the automation can identify bottlenecks and inefficiencies and automatically adapt to overcome those obstacles, human input and interaction could become almost obsolete.

In a recent interview, Christian Beedgen, CTO of Sumo Logic, shared some thoughts about the impact of machine learning on DevOps. “Machines will enable greater productivity among teams and businesses through the advances that machine learning and automation can offer,” he said. “A necessary component to augment human contributions, machine learning will finally give humans the power to review millions of bits of data.”

Automating routine tasks is crucial, but there’s another factor that plays into the role that AI and machine learning have with the future of DevOps. The reality is that there are some things humans simply can’t do as well or as fast as machines—especially at scale.

Steve Burton stressed in a blog post that humans are capable of observing, digesting and interpreting only so much information at one time. Some feel like the Holy Grail solution is some sort of application performance management (APM) dashboard that can aggregate and correlate real-time data so human IT personnel can monitor metrics and identify issues manually. “This all sounds good in theory, except it’s completely unmanageable in reality when your applications have thousands of components, billions of metrics and hundreds of changes every day,” he wrote. “Humans can no longer cope with this complexity, they now need machines to do the leg work, process this Big Data and provide operational insight into what the f**k is going on.”

Read the full story on DevOps.com: Machine learning, AI driving DevOps evolution.

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About Author

Tony Bradley is a social media, community, and content marketing wizard--and also Editor-in-Chief of TechSpective. Tony has a passion for technology and gadgets--with a focus on Microsoft and security. He also loves spending time with his family and likes to think he enjoys reading and golf even though he never finds the time for either.

2 Comments

  1. usm business Systems on

    3 reasons businesses should switch to DevOps

    1.Continuous collaboration
    2.Continuous integration
    3.Continuous delivery

  2. “Automating routine tasks is crucial, but there’s another factor that plays into the role that AI and machine learning have with the future of DevOps. The reality is that there are some things humans simply can’t do as well or as fast as machines—especially at scale.” — From a marketers perspective, automating some aspects of our social media feed is crucial to saving time, especially if you’re a startup. The fear with automation is that you’re probably losing out on true engagements.

    With the Atomic Reach platform we’re using machine learning to help content marketers, editors, social media managers, and the like, become more accurate when it comes to their content strategies. We’re able to tell them what content has resonated with their audience by studying engagement data and we will be able to automate their social media workflow by automatically posting articles at the most optimal times for social media engagements. (If you’d like to know more you can check out: http://www.atomicreach.com/)

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