How To Use Kubernetes in AI Projects

How To Use Kubernetes in AI Projects

What are the benefits of using Kubernetes in AI projects? Take a look at real-world use cases where Kubernetes helped optimize AI project performance and costs.

The Cloud Native Survey, which polls Technology/Software organizations, reports  that the use of containers in production has increased by 84% from the previous year, up to 92%. The use of Kubernetes is up 78% from the previous year, reaching 83%.

Kubernetes’ distributed architecture and scalability pair well with Machine Learning and Artificial Intelligence. As these technologies continue to mature, 2021 is the year to watch for growth in this space.

Companies should keep in mind that tools support business goals, and are not an end to themselves. 2020 was a year in which nearly every business was forced to reckon with unexpected changes. Kubernetes has the capability to accelerate the application development services through solutions built with a cloud-native ecosystem, while at the same time allowing for malleable use of applications and data so that a company can succeed through the modernization of its platforms and apps.

Below there are two real-world use cases of how Kubernetes helped optimize AI project's performance and costs.

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