Kubeflow Introduction is a comprehensive two-day course that helps data and technology professionals take machine learning workflows from ad hoc to production. The course gives participants a solid understanding of Kubeflow, a framework for deploying machine learning workflows on Kubernetes. Day one grounds attendees in Kubeflow and Kubernetes fundamentals before walking through deployment and configuration, then managing the platform’s running components day to day. The first day closes with Kubeflow Pipelines, where participants automate multi-step workflows so experiments run consistently instead of depending on manual, error-prone steps.
Day two puts that foundation into action. Attendees work directly in Kubeflow Notebooks to explore data and prototype models interactively. They then tune hyperparameters with Katib to search for better model configurations automatically rather than by hand. The course closes by training and serving models. This gives participants a complete, repeatable path from raw data to a model running in production and monitored once it gets there.
This course provides the understanding and skills needed to elevate machine learning workflows. It draws on some of today’s most advanced, scalable tools, whatever a team’s current level of MLOps maturity.
Who Should Attend
Developers, Data Scientists, Data Engineers, Machine Learning Engineers, MLOps professionals
What Attendees Will Learn
Upon completing Kubeflow Introduction, participants will be able to:
- Gain an in-depth understanding of Kubeflow, from its architecture, deployment and use
- Acquire hands-on configuring and managing Kubeflow and its components
- Learn how to use Kubeflow Notebooks and Pipelines to build machine learning workflows
- Understand how to use Kubeflow to train and deploy models
- Develop the skills and expertise to transform machine learning workflows into production grade end-to-end solutions
Prerequisites
Participants should have intermediate programming skills (preferably in Python), an understanding of mathematics and statistics, basic familiarity with machine learning, a grasp of computer science fundamentals, familiarity with containers, Kubernetes, and basic command-line operations, and a willingness to review pre-course materials.
These prerequisites will help participants engage effectively with the course material and hands-on labs, making the learning experience more rewarding.