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, 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.
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.
Course Overview [Autogenerated] Hi, everyone. My name is appreciate. You are welcome to my course on building into a machine learning work through with cute I'm a data science consultant. Google developers export for machine learning and also graduate from UC Berkeley. Machine learning and deep learning work flows are treated and complex in nature, and traditional software tool kits and practices are inadequate. Tackle this complex city in a scalable and robust fashion. In this course, we're going to build an end to end machine learning workloads Project using various components and features are open source Que Flu ecosystem covering entire spectrum from training to tuning, to serving and monitoring and to building reproducible pipelines. Some of the major topics that will cover include interaction, tokyu flu and how to use companies off. Q. Flew to perform activities such as training at scale and launch and track scalable hyper perimeter tune experiments. And sitting up several Ismail serving and monitoring and finally building reproducible pipeline toe tie back all of the steps in a workflow. By the end of the schools, you will be able to build portable and scalable end to invert flew for your machine learning and deep learning projects I hope you will join me on this journey to build production grade and Enterprise City machine learning workflow With this Q flow courts that brutal side.