Today, containerization has transformed how we deploy software and work with microservices. As the trend of working with Linux-based, virtual containers for developing applications continues to evolve, it has brought in higher demands for their management and deployment.
Kubernetes and Docker are two of the major players in container orchestration. They have carved reputable niches for themselves and cemented their positions in the Docker and the container ecosystem. Both of these tools enable you to handle a cluster of servers that run one or more services on them. So, before jumping into the comparison part, let’s get an overview of these two tools.
AIOps: Is DevOps Ready for an Infusion of Artificial Intelligence?
This article is a post in a series on bringing continuous integration and deployment (CI/CD) practices to machine learning. Check back to The New Stack for future installments.
With orchestration and monitoring playing such key roles in DevOps, the emerging trend of using artificial intelligence (AI) to support and even automate operations roles by delivering real-time insights about what’s happening in your infrastructure seems an obvious fit.
DevOps is about improving agility and flexibility; AIOps should be able to help by automating the path from development to production, predicting the effect of deployment on production and automatically responding to changes in how the production environment is performing. That’s especially true as trends like microservices, hybrid cloud, edge computing and IoT increase the complexity of app infrastructures — and the number of logs that you might have to look at to find the root cause of an issue, and the number of people who need to be in a conference call or chat room tracking down what’s gone wrong and how to fix it.
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