Introduction to Kubeflow (en)
The ‘Introduction to Kubeflow’ course provides a comprehensive overview of the installation, use and customization of Kubeflow, an Open-Source toolkit designed to simplify the development, training and deployment of machine learning models on Kubernetes. During the course, students will be introduced to the basic concepts of Kubeflow and the process of installing it in a Kubernetes cluster. Then, they will be guided through interaction with Kubeflow Notebooks for developing machine learning models.
The course will also cover the creation of custom images for creating custom notebooks. Using KServe to deploy models in production environments. Students will also explore the Discovery Pipeline and experiments for machine learning workflow management and hyperparameter optimization.
The course will culminate with a hands-on demonstration on customizing the Kubeflow dashboard and managing the entire lifecycle of a model from an MLOps perspective.
CODE: DSAI202
Category: Artificial Intelligence
Teaching methodology
The course includes educational laboratories in which each student will be able to work in order to complete training exercises that will provide practical experience in using the instrument, for each of the topics covered during the course.
Prerequisites
- Basic knowledge of containerization and Kubernetes concepts.
- Familiarity with the Linux environment.
- Familiarity with bash.
- Understanding of machine learning fundamentals.
- Familiarity with Python and the ecosystem of machine learning libraries.
The following is an overview of course content:
- Installing Kubeflow
- Using Notebooks
- Custom Images for Notebook Servers
- KServe in Kubeflow
- Pipelines and Experiments in Kubeflow
- AutoML in Kubeflow
- Customizing the Kubeflow Dashboard
- MLOps Concepts
Upon completion of the course, participants will be able to:
- Understand the installation and configuration of Kubeflow.
- Interact with the notebook server.
- Create and use custom images for use within notebooks.
- Experiment with KServe.
- Use the discovery pipeline and conduct experiments.
- Optimize hyperparameters.
- Customize the dashboard.
- Gain fundamental concepts of MLOps.
Duration – 1 day
Delivery – in Classroom, On Site, Remote
PC and SW requirements:
- Internet connection
- Web browser, Google Chrome
- Zoom
Language
- Instructor: English
- Workshops: English
- Slides: English
