Last updated 4/2020MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 ChLevel: Bner | Genre: eLearning | Language: English + vtt | Duration: 3h 30m | Size: 512 MB

Building production grade, scalable machine learning workflows is a complex and -consuming task. In this course, Building End-to-end Machine Learning Workflows with Kubeflow 1, you will learn to use Kubeflow and discover how it can enable data scientists and machine learning eeers to build end-to-end machine learning workflows and perform rapid expentation. First, you will delve into perfog large scale distributed training. Next, you will explore hyperparameter tuning, model versioning, serverless model serving, and canary rollouts. Finally, you will learn how to build reproducible pipelines using various Kubeflow components, such as notebook server, fairing, metadata, katib, and Kubeflow pipelines. When you are finished with the course, you will be able to build end-to-end workflows for your machine learning and deep learning projects.

HomePage: