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Building End-to-end Machine Learning Workflows with Kubeflow 1
In this course, you will learn how to effectively build end-to-end scalable, production grade machine learning workflows using Kubeflow.
What you'll learn
Building production grade, scalable machine learning workflows is a complex and time-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 engineers to build end-to-end machine learning workflows and perform rapid experimentation. First, you will delve into performing 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.
About the author
Abhishek Kumar is a data science consultant, author, and Google Developers Expert (GDE) in machine learning. He holds a master’s degree from the University of California, Berkeley, and has been featured in the "Top 40 under 40 Data Scientist" list.