Introduction to AI/ML Toolkits with Kubeflow (LFS147)
Explore the origins, deployment options, individual components and common integrations of Kubeflow.
Who Is It For
This course is designed for developers, engineers, data scientists or anyone interested in understanding the anatomy of a machine learning tool kit that harnesses the power of Kubernetes.
By the end of this course, you will understand Kubeflow’s architecture and key components and know how to prepare data, model training, serving, and management within Kubeflow.
You will be ready to deploy real-world ML projects using Kubeflow as well as have the skills and knowledge needed to contribute to the Kubeflow community.
Course OutlineWelcome!Chapter 1. The Model Application Relationship and the Power of ReproducibilityChapter 2. The Model Development LifecycleChapter 3. MLOPs and the Rise of the Machine Learning ToolkitChapter 4. The Origin of KubeflowChapter 5. Kubeflow DistributionsChapter 6. The Kubeflow Dashboard and NotebooksChapter 7. The Unified Training Operator and Machine LearningChapter 8. Kubeflow PipelinesChapter 9. Conquering KatibChapter 10. Common Kubeflow Integrations
Prerequisites
To make the best of this course, you will need to have the following:
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
Reviews
Sep 2024
The course content and delivery was simply excellent! From the layout to the concision of information, which covered relevant subjects/areas in a waffle-friendly manner. Top marks, a great entry point for beginners and avid tech enthusiasts such as myself!
Jun 2024
I like the easy explanations.
Jun 2024
Overall, the course is a good introductory Kubeflow course, helping folks get started on their Kubeflow journey.
Jun 2024
Every detail in the course helps me to learn quickly.
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