The Kubeflow project makes deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Kubeflow comes with Jupyter notebook, training and inference using Tensorflow, hyperparameter tuning using Katib, end-to-end automated deployment pipelines using Argo, hyperparameter tuning using Katib, and much more. This talk will explain why and how Kubernetes is well suited for single- and multi-node distributed training, training your models, and deploying your models for inference in production. Specifically it will show how to use KubeFlow and TensorFlow for your machine learning needs. We will also show to setup machine learning pipelines and set up visualization tools like TensorBoard for monitoring. We will also discuss distributed training using Horovod.

#machine_learning #Kubernetes #Kubeflow