Machine Learning as Code: and Kubernetes with Kubeflow
Machine Learning is become an increasingly popular topic in the world of data. At the same time, the concept of microservices through containerization has increased in popularity as it allows for deve …
| Talk Title | Machine Learning as Code: and Kubernetes with Kubeflow |
| Speakers | David Aronchick (Head of OSS Machine Learning, Microsoft), Jay Smith (Cloud Customer Engineer 云客户工程师, Google) |
| Conference | KubeCon + CloudNativeCon North America |
| Conf Tag | |
| Location | Seattle, WA, USA |
| Date | Dec 9-14, 2018 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Machine Learning is become an increasingly popular topic in the world of data. At the same time, the concept of microservices through containerization has increased in popularity as it allows for developers to create and package applications for easy export and distribution through various clouds.Kubeflow is an open source project lead by Google to merge both concepts, allowing users to leverage the power of Kubernetes to run the training and serving of their ML models.This convergence of technologies does result in a new way to think of Machine Learning. We now think of can think of machine learning as code bundles. My session will show how with Kubeflow and GitOps tools, you can go beyond simply deploying and training TensorFlow models but bundling the entire infrastructure into a code package and treat the entire machine learning process as a pipeline.
