In this blog post, we explore groundbreaking research on optimizing production ML inference systems to achieve high accuracy while minimizing costs. A collaboration between researchers from multiple institutions has resulted in the development of three adaptive systems - InfAdapter, IPA, and Sponge - that tackle the accuracy-cost trade-off in complex, real-world ML scenarios. Learn how these solutions, implemented on the Chameleon testbed, are pushing the boundaries of cost-effective ML inference and enabling more accessible and scalable ML deployment.