Hyperparameter Tuning Using Kubeflow

Explore hyperparameter tuning and neural architecture search using Katib, a Kubernetes-native automated machine learning platform within Kubeflow. Learn how to optimize model performance by finding optimal constraints for training, and discover how networks generated by NAS algorithms can outperform handcrafted neural networks. Dive into Katib's rich set of management APIs, configure and run experiments, and compare performance using the UI dashboard. Follow along with demonstrations on setting up experiments, configuring search spaces, and viewing results and trial metrics. Gain insights into the landscape of automated machine learning, understand the workflow for neural architecture search, and learn about future developments and opportunities to contribute to this cutting-edge technology.