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4 Things Machine-Learning Algorithms Can Do for DevOps

In this special guest feature, Ronny Lehmann, CTO at Loom Systems, discusses how digital transformation has created challenges and benefits for DevOps and the constant pressure of picking up the pace in the endless ocean of complexity can often seem impossible. With machine learning and AI, the fundamental necessity to ensuring a high-quality customer experience can become more of a reality. Ronny began his career with 8 years in the IDF’s elite 8200 unit leading R&D teams in cyber and algorithms projects and implementations. Prior to joining Loom Systems, Ronny was VP of R&D at BioCatch, a leading provider of behavioral biometric, authentication and malware detection solutions through machine learning and signal processing.

As the IT industry struggles to elevate performance, companies are looking to DevOps to deliver on the promise of a newly efficient process that includes frequent release cycles to feed the higher demanding consumer. However, speeding up release cycles is far easier said, than done. IT practitioners are constantly seeking new tools to improve their responsiveness to business needs in this new environment.

Transforming to a digital environment can prove to be a difficult evolution for most enterprise organizations; fruition will require a new mindset that is facilitated by technological progress.

Despite the relatively slow adoption rate in the field of machine-learning, DevOps practitioners have a lot to gain by embracing even the most basic of capabilities that artificial intelligence (AI) has to offer. In fact, whether or not to embrace AI is no longer the question but rather where to start.

There are a number of machine-learning dependents that are starting to be relied upon by the DevOps community.

Maintaining an Alerting Rule-engine

An ongoing and repetitive task (aka mundane), which almost always goes out of control when done manually. Maintaining the alerting “rule-engine” is a classic fit for the smart machine – to do it right, one needs to take into consideration inter-dependencies, baseline behavior; and to do all this continuously. Machines can also dig deeper, identify changes in trends and eliminate bend-before-break situations that aren’t feasible to detect with today’s arsenal.

Prioritizing Alerts

Manually prioritizing alerts in today’s flooded environment is like rearranging the deck chairs on the Titanic. The capacity that smart machines have to apply parameters to prioritization is virtually endless and dynamic. Alerts are analyzed for significance in real time and machines correlate the alert to everything the application or component is related to and affected by. This level of sophistication enables DevOps to use alerts proactively and precisely.

Analyzing the Root Cause

The importance of finding the root cause of a problem cannot be stressed enough. Many of us have learned the significance of clearing “the fog of war” – the countless number of alerts (and complaints) one gets when something breaks. Machines can help clear the fog by drawing maps of correlations and causality between the various inputs. Machine learning enables DevOps to cut through the noise and determine the sources and correlation of all alerts.

Recommendation Algorithms

Although there are, in fact, many tasks and processes that can be smartly automated to enable DevOps to do their job, recommendation algorithms is definitely one of the magic makers.  Smart machines will go beyond mundane task excellence to provide answers. Receiving alerts that are relevant, correlated and prioritized is one thing, but having them delivered alongside the relevant resolution is a whole new level of efficient for DevOps.

Digital transformation has created challenges and benefits for DevOps. However, the constant pressure of picking up the pace in the endless ocean of complexity can often seem impossible. With machine learning and AI, the fundamental necessity to ensuring a high-quality customer experience can become more of a reality.

 

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