The Kubeflow course is a comprehensive training program designed to equip learners with the skills necessary to deploy, manage, and scale machine learning workflows using Kubeflow on Kubernetes. Whether the learners are interested in Kubeflow training for personal advancement or professional development, the course covers a wide range of topics, from the basics of Kubernetes to the sophisticated techniques of Kubeflow distributed training.
Starting with an introduction to Kubernetes, the course lays the foundation needed to understand Kubeflow's interaction with container orchestration. As the course progresses, learners will explore Kubeflow's features and architecture, and how it can be implemented on AWS, on-premise, and on other public cloud providers. Practical modules guide students through setting up clusters, creating and managing Kubeflow pipelines, Hyperparameter tuning with TensorFlow, and scaling with Multi-GPU training.
By understanding Data storage approaches, creating Inference servers, and utilizing JupyterHub within the Kubeflow ecosystem, learners will gain hands-on experience. The course also addresses critical operational skills, such as Networking, Load balancing, Auto-scaling, and Troubleshooting. Completing the course will empower students with a solid understanding of Kubeflow, positioning them to effectively deploy machine learning workflows at scale.
