Explore privacy budget scheduling in machine learning through this OSDI '21 conference talk. Delve into the challenges of managing differential privacy (DP) in model training to prevent information leakage. Learn about PrivateKube, an extension to Kubernetes that treats privacy as a manageable resource alongside traditional compute resources. Discover the Dominant Private Block Fairness (DPF) algorithm, designed to handle the non-replenishable nature of privacy budgets. Examine the talk's evaluation of PrivateKube and DPF on microbenchmarks and an ML workload, demonstrating how DPF allows for training more models under the same global privacy guarantee. Gain insights into the complexities of balancing privacy concerns with machine learning model development in this informative presentation.