A Deep Dive Into AIOps and MLOps
This is an article from DZone’s 2023 DevOps Trend Report.
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Monitoring and managing a DevOps environment is complex. The volume of data generated by new distributed architectures (such as Kubernetes) makes it difficult for DevOps teams to effectively respond to customer requests. The future of DevOps must therefore be based on intelligent management systems. Since humans are not equipped to handle the massive volumes of data and computing in daily operations, artificial intelligence (AI) will become the critical tool for computing, analyzing, and transforming how teams develop, deliver, deploy, and manage applications.
What Are Machine Learning Operations?
Machine learning operations (MLOps) refers to the lifecycle management of machine learning (ML) projects. It is a key concept of modern machine learning application development, and its purpose is to make the training, deploying, and maintaining of machine learning applications seamless and efficient. MLOps is not a set of specific technologies but rather an umbrella term for activities focused on building reliable and well-functioning machine learning models. It includes both development work practices and ways of working as a project team — essentially functioning as a set of best practices for machine learning application development.