Open Source Summit + ELC North America 2019

Though machine learning and AI are immensely powerful, these solutions are by no means easy. In many cases, there are many diverse components that are not designed to work together. Additionally, these models are most efficient when running on large scale clusters that can be more difficult to manage. Configuration and deployment is often left to data scientists who are wasting time on infrastructure and not on data science itself.

Kubernetes to the rescue! In this session I will talk about how machine learning can be greatly improved by implementing ML solutions on top of Kubernetes with containers. I will be discussing each stage of a typical workflow including: data preparation/versioning, model training, testing and validation, monitoring, and CI/CD and automation. Demos will include tooling such as Tensorflow/Kubeflow, Pachyderm, Argo, etc.

This talk is for both data scientists and infrastructure/SRE teams alike helping bring the benefits of DevOps to AI and machine learning.