What the Paradigmatic Shift in Machine Learning Means for DevOps

Artificial intelligence (AI) and machine learning (ML) have been under study for years. But what is new is the tremendous impact and overlap AI and ML are now having on application development. Among the benefits, we will continue to see applications based on massive amounts of data sets that make use of human brain-like neural network computing to perform tasks in minutes or seconds that previously required thousands of human hours to perform. On a more practical level, AI is used to automate some of the more rudimentary tasks in software production pipelines, while freeing up time for developers to focus on more creative and intellectually rewarding work. ML and AI fit are also increasingly leveraging the resources and ability to rapidly scale Kubernetes, and more specifically, Kubeflow offer. During a podcast from the recently held from KubeCon + CloudNativeCon China, Alex Williams, founder and editor in chief of The New Stack, spoke with Dr. Han Xiao, engineering lead at Tencent AI Lab, and Alejandro Saucedo, chief scientist at the Institute for Ethical AI and machine learning, to learn more leveraging AI’s and ML’s power for today’s at-scale application development and deployments.