Why use Machine Learning in DevOps?

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Machine Learning (ML): is an application of Artificial Intelligence which allows systems to learn and improve from experience, rather than being manually programmed for each instance.

DevOps: is a software engineering practice that aims to unify software development and software operation.

Whilst DevOps has improved software development exponentially, from increasing both qualities to productivity, there are still limitations. The relationship between ML and DevOps is evident, and it is capable of enhancing organisations’ ability to manipulate and analyse large amounts of data far more accurately and rapidly than a human engineer.

ML is able to handle the volume, amount and variety of data through its agile capabilities to learn from and process data far quicker and more accurately than its human counterparts. What’s required for the processes to run smoothly, is "an integrated lifecycle view of a project", deeper than is possible than between physical team members working on a task. At the Machine Learning for DevOps Summit in Houston this November 29 and 30, RE•WORK will bring together organisations and individual experts applying ML to transform their DevOps workflows and enhance automation capabilities.

The adoption of ML is being used more an more in several spaces, and we’re taking a look at 5 of the key areas which will be covered and explored in depth at the summit:

Troubleshooting and Triage Analytics

ML can automatically detect and even start to intelligently triage ‘known issues’, as well as some unknown issues. For example, ML tools can detect anomalies in ‘normal’ processing, and then further analyze release logs to correlate this issue with a new configuration or deployment. Other automation tools can use ML to alert operations, open a ticket (or a chat window), and assign it to the right resource. Over time, ML may even be able to suggest the best fix!

Preventing Production Failures

There are plenty of examples of how devops works well and delivers tangible improvements. But sometimes it doesn't work well. Things can go wrong with devops just as they can with any other aspect of IT. ML can go well beyond straight-line capacity planning in preventing failures. ML can map known good patterns of utilization to predict, for example, the best configuration for a desired level of performance; how many customers will use a new feature; infrastructure requirements for a new promotion; or how an outage will impact customer engagement. ML sees otherwise opaque ‘early indicators’ in systems and applications, allowing Ops to start remediation or avoid problems, much faster than typical response times.

Ensuring Application Delivery

Patterns of user behaviour can be as unique as fingerprints. Applying ML to Dev and Ops user behaviours can help to identify anomalies that may represent malicious activity. For example, anomalous patterns of access to sensitive reports, automation routines, deployment activity, test execution, system provisioning, and more can quickly highlight users exercising ‘known bad’ patterns — whether intentionally or accidentally — such as coding back doors, deploying unauthorized code, or stealing intellectual property.

Managing Production

Analyzing an application in production is where machine learning really comes into its own, because of the greater data volumes, user counts, transactions etc. that occur in prod, compared to dev or test. DevOps teams can use ML to analyze ‘normal’ patterns — user volumes, resource utilization, transaction throughput, etc. — and subsequently to detect ‘abnormal’ patterns (e.g. DDOS conditions, memory leaks, race conditions, etc.).

Alert Storms

Humans alone cannot deal with all the noise. Fortunately, smart technology is being developed to enhance organizations’ ability to parse through extensive troves of data as they find the insights they need to be successful.

Think you should be applying ML in your DevOps team? Join us in Houston this November 29 - 30 to learn from over 60 expert speakers working in the space, and network with over 450 likeminded attendees.

Confirmed speakers include: Chandini Sharma, Cloud Engineer at Google, Diego Oppenheimer, Founder & CEO at Algorithmia, Clint Wheelock, Founder & Managing Director at Tractica, Pooyan Jamshidi, Assistant Professor at University of South Carolina, Adam McMurchie, DevOps Solutions Manager at RBS and more.

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Machine Learning AI Business Applications


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