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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. 

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Open-Source Tools to Use on an On-Prem Kubernetes Cluster

Kubernetes has dramatically shifted the trade-offs of on-prem versus SaaS deployments. Thanks to the rich abstractions Kubernetes provides, deploying software on-premises can be significantly easier than it used to be. Because Kubernetes has achieved such high market penetration (and still growing), it is now a viable target environment for many software products. Nevertheless, Kubernetes requires external tools to be production ready, especially on an on-prem deployment.

The purpose of this article is to list tools that everyone should be aware of when it’s time to move an on-prem Kubernetes cluster to production, and by on-prem, we mean not in a cloud environment. In the cloud, it is obviously better to rely on cloud services offered by the provider.

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Security and GitOps

As we all know and firmly believe, applications and infrastructures need to be secured, but the shipping processes of this whole ecosystem also need to be.

In a previous article, we introduced GitOps as a methodology to improve the velocity of the development and the management of an entire infrastructure. But there are many other benefits from GitOps, and one of them is the potential improvement of security.