ML workflows with Kubeflow
Deploying machine learning pipelines robustly at scale is one of the biggest challenges within an organization. Kubeflow is an open-source platform for distributed training, tuning, and serving models on Kubenetes. As a comprehensive solution for deploying and managing end-to-end data science and machine learning pipelines, Kubeflow is rapidly accelerating analytics innovation and adoption. John will provide an overview of Kubeflow and how he has been using it in the wild.
John Liu is passionate about building machine learning solutions to solve business problems. He is founder and CEO of Intelluron Corporation, an emerging AI-based analytics company. Most recently, John was VP of Data Science, Applied Machine Learning at Digital Reasoning Systems. He is co-author of the recently published book Deep Learning for NLP and Speech Recognition. In 2016, John was named Nashville’s Data Scientist of the Year. He earned his B.S., M.S., and Ph.D. from the University of Pennsylvania and is a CFA Charterholder.
Kevin Crawley will configure a RBAC-enabled vanilla K8S cluster in GKE, deploy Prometheus and Jaeger in support of observing and monitoring a distributed microservice application, instrument that application by introducing libraries and tooling for capturing metrics and distributed traces. All code used during this talk will be provided for your use.