Machine learning is about being able to process large data sets quickly and efficiently. Capabilities such as parallelization of jobs, data segmentation, hardware abstraction, and batch processing are critical for machine learning.

These capabilities are supported natively in Kubernetes and as a result, it is extremely well suited for machine learning. This paper explores the key challenges that organizations experience supporting these initiatives, as well as best practices for successfully leveraging Kubernetes to accelerate AI/ML projects.