We implement several popular ML applications using Litz, and show that they can scale in and out quickly to adapt to changing resource availability, as well as how a scheduler can leverage elasticity for faster job completion and more efficient resource allocation. Lastly, we show that Litz enables elasticity without compromising performance, achieving competitive performance with state-of-the-art non-elastic ML frameworks.
