Source: thenewstack.io
3 Important AI/ML Tools You Can Deploy on Kubernetes
Things move fast in the world of infrastructure technology. Builders of cloud native applications have gotten good at running stateful workloads, because Kubernetes is a powerful way to create virtual data centers quickly and efficiently.
What’s driving this is the need for faster and more agile MLOps to support https://huyenchip.com/2022/01/02/real-time-machine-learning-challenges-and-solutions.html, otherwise known as real-time artificial intelligence (AI).
Recent https://dok.community/data-on-kubernetes-2022-report/ show that leaders in the data space are finding both when deploying data infrastructure in Kubernetes.
The bigger picture for AI/ML in the application stack: Real-time requirements will soon become prevalent in most AI applications.
What’s driving this is the need for faster and more agile MLOps to support https://huyenchip.com/2022/01/02/real-time-machine-learning-challenges-and-solutions.html, otherwise known as real-time artificial intelligence (AI).
Recent https://dok.community/data-on-kubernetes-2022-report/ show that leaders in the data space are finding both when deploying data infrastructure in Kubernetes.
The bigger picture for AI/ML in the application stack: Real-time requirements will soon become prevalent in most AI applications.
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