Using Machine Learning to Optimize DevOps Practices


As we move into a world where decisions are being made by algorithms, the focus on machine learning systems is one of the central tenets around big data, analytics, and artificial intelligence surrounding a wide variety of industries, from self-driving cars to ecommerce engines.

DevOps is about breaking down barriers between development and operations. The most important way of breaking down barriers is to share information, so that everyone can understand the state of the process and help resolve problems. Because of the amount of information created in DevOps is massive, machine learning systems can monitor the process and make recommendations for improvements and remediation.

This presentation describes how machine learning systems can be used as an expert analysis engine for data collected during build, deployment, and production. It can evaluate how applications are developed to determine process improvements, as well as predict issues and offer advice on how to resolve them.

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Speaker

peter-varhol

Peter Varhol

 

Peter Varhol is a well-known writer and speaker on software and technology topics, having authored dozens of articles and spoken at a number of industry conferences and webcasts. He has advanced degrees in computer science, applied mathematics, and psychology, and is currently a community evangelist at Dynatrace, an international software testing and application monitoring company. His past roles include technology journalist, software product manager, software developer, and university professor.