Kubernetes: Simplifying Machine Learning Workflows
Kubernetes: Simplifying Machine Learning Workflows

Abstract: 

You know Kubernetes is a great platform for the applications you’re running today. Most of the applications you’ll be excited about tomorrow are intelligent applications, which collect data and rely on machine learning to support essential functionality. These capabilities often seem like magic to users, but building applications and services that leverage artificial intelligence is more accessible than you might think. This workshop will show how Kubernetes, the most popular open source container orchestration platform, can increase collaboration and decrease time to value for machine learning workflows. Attendees will be able to create workflows with ease, deploy models as micro-services and monitor performance to understand when retraining is needed. We’ll focus on the open source infrastructure, tools and processes that will help you to get meaningful results from application intelligence and show why Kubernetes is the best place for data science workloads. You’ll leave having solved a real business problem interactively with powerful machine learning techniques and Kubernetes.

Bio: 

William Benton leads a team of data scientists and engineers at Red Hat, where he has applied analytic techniques to problems ranging from understanding infrastructure logs at datacenter scale to designing better cycling workouts. His current focus is designing architectures for machine learning systems in the hybrid cloud, but he has also conducted research and development in the areas of static program analysis, managed language runtimes, logic databases, cluster configuration management, and music technology.