Kubeflow Fundamentals Training Course

Kubeflow serves as a comprehensive toolkit designed to streamline Machine Learning (ML) workflows on Kubernetes, ensuring they are effortless to deploy, highly portable, and capable of scaling effectively.

Delivered as an instructor-led, live session—available either online or onsite—this course is tailored for developers and data scientists eager to construct, deploy, and oversee machine learning operations within a Kubernetes environment.

Upon completion of this training, participants will possess the ability to:

• Install and configure Kubeflow in both on-premise and cloud-based environments.
• Architect, deploy, and manage ML workflows leveraging Docker containers and Kubernetes.
• Execute complete machine learning pipelines across various architectures and cloud platforms.
• Leverage Kubeflow to initiate and manage Jupyter notebooks.
• Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

Course Format

• Engaging lectures paired with interactive discussions.
• An extensive range of exercises and practical tasks.
• Hands-on implementation within a live-lab setting.

Course Customization Options

• To request a customized version of this training, please reach out to us to make arrangements.
• For further insights into Kubeflow, please visit: https://github.com/kubeflow/kubeflow