Learning path
Kubeflow
Skill Level
Introductory
Duration 6 hours
Updated Jun 15, 2026
About this learning path
Kubeflow is an open-source machine learning platform dedicated to making deployments of machine learning workflows on Kubernetes simple, portable, and scalable. This learning path is structured to provide both theoretical knowledge and practical, hands-on experience with the core components of the Kubeflow ecosystem.
Your instructors
Prerequisites
- Kubernetes Basics: Familiarity with core Kubernetes concepts (Pods, Deployments, Services, Persistent Volumes).
- Machine Learning Fundamentals: A basic understanding of the ML lifecycle (data preparation, training, evaluation, inference, hyperparameters).
- Programming & Scripting: Basic proficiency in Python and familiarity with the Linux/Unix command line.
What you'll learn
- Understand ML Platforms: Learn the value proposition and core concepts of Kubeflow.
- ML Infrastructure: Configure a Kubernetes cluster optimized for AI/ML workloads and deploy the Kubeflow platform.
- Manage Interactive Workspaces: Provision and manage Jupyter Notebook environments for data exploration and model development.
- Automate ML Workflows: Transform machine learning workflows into automated, reproducible pipelines using Kubeflow Pipelines.
- Scale Model Training: Execute distributed training jobs for popular frameworks and perform automated hyperparameter tuning using Katib.
- Process Data: Run Apache Spark data processing workloads natively on Kubernetes.
- Manage the ML Lifecycle: Version, store, and discover model artifacts using Kubeflow Hub.
- Integrate Ecosystem Tools: Extend your ML platform capabilities with ecosystem tools like Feast and Elyra.