Explore a comprehensive conference talk that provides an in-depth look at the daily life of a data scientist and the machine learning lifecycle on Kubernetes. Discover how to conquer the challenges of ML development using open-source tools like Kubeflow. Follow along as Rita Zhang and Brian Redmond from Microsoft guide you through code collaboration, dataset preparation, training, and serving. Gain practical insights into utilizing Kubernetes for ML solutions, including real-world examples, transfer learning, and hyperparameter tuning. Learn how DevOps principles can be applied to AI and machine learning projects, making this talk valuable for both data scientists and infrastructure/SRE teams. Witness a demo of serving models and explore concepts like CoopFlow. Conclude with a recap and next steps to further enhance your understanding of the ML lifecycle on Kubernetes.