Machine learning (ML) applications are complex to deploy and often require multiple ML models to serve a single inference request. A typical request may flow across multiple models with steps like preprocessing, data transformations, model selection logic, model aggregation, and postprocessing. This has led to the evolution of common design patterns such as serial inference pipelines, ensembles (scatter gather), and business logic workflows, resulting in realizing the entire workflow of the request as a Directed Acyclic Graph (DAG). However, as workflows get more complex, this leads to an increase in overall response times, or latency, of these applications which in turn impacts the overall user experience. Furthermore, if these components are hosted on different instances, the additional network latency between these instances increases the overall latency. Consider an example of a popular ML use case for a virtual assistant in customer support. A typical request might have to go through

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