Data processing pipelines are becoming increasingly complex, and intermediate data sharing is becoming the bottleneck, especially for data-intensive analytics and data preprocessing in machine learning and AI.

This blog shows the possibility of efficient data sharing in data science pipelines, which naturally fits the settings of Kubernetes. It demonstrates how existing codebases can benefit from it without requiring an overhaul of the engineering effort.