Machine learning (ML) should have a profound effect on many facets of our lives in the future, from self-driving cars and trucks to utility grid management. As artificial intelligence (AI) engineers and data scientists develop advanced systems based on neural networks and other technologies designed to teach machines to learn and act in ways similar to the human brain, workflows that integrate all facets of the underlying software development and deployments for ML applications will play an obvious and critical role.

To that end, David Aronchick, Co-Founder of Kubeflow, described how Kubeflow can make setting up machine learning software production pipelines easier, during a podcast, Alex Williams, founder and editor-in-chief of The New Stack, recorded at KubeCon + CloudNativeCon 2018 in Shanghai.

Watch on YouTube: https://youtu.be/pdkXhXmJoK8