Using Machine Learning to Automate Kubernetes Optimization

Note: This is the third of a five-part series covering Kubernetes resource management and optimization. In this article, we explain how machine learning can be used to manage Kubernetes resources efficiently. Previous articles explained Kubernetes resource types and requests and limits. As Kubernetes has become the de-facto standard for application container orchestration, it has also raised vital questions about optimization strategies and best practices. One of the reasons organizations adopt Kubernetes is to improve efficiency, even while scaling up and down to accommodate changing workloads.