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Learn about Kubeflow, the open source, CNCF-backed, Kubernetes-native, scalable, and portable machine learning toolkit.
Machine learning and AI are rapidly transforming the world, impacting organizations of all sizes. As executives push for AI/ML strategies, DevOps teams have been upskilling and bridging the gap between operations and development for the last several years for traditional applications. The complex machine learning application arrives just as cross-team collaboration becomes familiar.
These data-dependent applications present fresh challenges for deployment and development, demanding expertise from developers and data scientists, data engineers, and machine learning engineers. How can existing engineers, with their container, Kubernetes, and cloud knowledge, navigate this terrain? Can non-engineers seeking smoother data-intensive projects find common ground with statistically-savvy data scientists? We think so! Enter Kubeflow, an open source, Kubernetes-powered toolkit that enables teams of any scale or maturity to harness the potential of machine learning. Rather than reinventing the wheel, Kubeflow simplifies the deployment of proven open-source ML systems across any cloud and even on-premise
This course begins with Kubeflow, covering its origins, deployment options, individual components, and standard integrations. By the end, you'll grasp how MLOPs can ensure the successful production of ML systems, how Kubeflow opens up ML for everyone, regardless of scale, understand how to choose the ideal Kubeflow distribution for your needs so you can see Kubeflow’s "simple, portable, scalable" promise in action, and launch your own Kubeflow project. We will even touch upon some additional open source integrations so you can make Kubeflow work for you!
This course caters to everyone wanting to leverage the power of machine learning. Whether you're an engineer, data scientist, or simply curious about Kubeflow, join us and discover how you can contribute to the future of machine learning!
To make the most of this course, you should have:
Experience with cloud computing
Familiarity with DevOps and cloud native principles
Basic programming experience
Experience with technical documentation
Experience with open source projects in general.
Basic understanding of Kubernetes might be helpful but not necessary
Discuss the value of MLOPs for production systems and how it relates to DevOps
Recognize common machine learning platform patterns and the problems they seek to solve
Explain the model development lifecycle
Define and identify common machine learning frameworks
Discuss the value proposition and goal of the universal training operator
Research and select a Kubeflow distribution based on your needs or, at the very least, have an informed conversation with a vendor.
Launch and leverage a Kubeflow Notebook.
Launch a primary Kubeflow pipeline.
Discuss additional popular Kubeflow integrations.
Familiarize yourself with Katib and Hyperparameter tuning
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
