This article talks about how Kubernetes has emerged from container orchestration platform to manage complex workloads in AI and Machine Learning Stacks, Managing containers in NFV architecture and handling hardware GPU resources.
In the past few years, developers have moved en masse to containers for their ease-of-use, portability and performance. Today, we’re excited to announce that Google Cloud Platform (GCP) now offers container-native load balancing for applications running on Google Kubernetes Engine (GKE) and Kubernetes on Compute Engine, reaffirming containers as first-class citizens on GCP.
Containers are a hot topic, yet production usage remains low, according to Gartner, with most enterprise container adoptions in an early phase. A recent survey by Diamanti backs this up, finding that while almost half (47%) of the IT leaders it surveyed plan to deploy containers in a production environment, only 12% have already done so.
In the four years since we launched Google Kubernetes Engine (GKE), it has become a fast favorite among enterprises running large container-based applications in production. But even the biggest, most mission-critical system was once just a prototype, and even the largest organization was once just a startup.