Explore the integration of Automated Machine Learning (AutoML) pipelines using Argo Workflows and Katib in this 22-minute conference talk. Dive into the world of Kubernetes-native open-source projects for model selection and hyperparameter tuning. Learn how Katib can optimize hyperparameters for models across various frameworks, including Tensorflow, PyTorch, MXNet, and Scikit-learn. Discover the process of evaluating metrics after model training and how complex training workflows can be represented as dependency graphs. Understand the benefits of using Argo Workflows as a container-native workflow engine for orchestrating jobs on Kubernetes, and see how it seamlessly integrates with Katib Experiments. Gain insights into streamlining the MLOps lifecycle and enhancing your AutoML capabilities through this informative presentation by experts from Apple and Nutanix.