Machine learning (ML) model deployments can have very demanding performance and latency requirements for businesses today. Use cases such as fraud detection and ad placement are examples where milliseconds matter and are critical to business success. Strict service level agreements (SLAs) need to be met, and a typical request may require multiple steps such as preprocessing, data transformation, model selection logic, model aggregation, and postprocessing. At scale, this often means maintaining a huge volume of traffic while maintaining low latency. Common design patterns include serial inference pipelines, ensembles (scatter-gather), and business logic workflows, which result in realizing the entire workflow of the request as a Directed Acyclic Graph (DAG). However, as workflows get more complex, this can lead to an increase in overall response times, which in turn can negatively impact the end-user experience and jeopardize business goals. Triton can address these use cases where multiple models are composed in

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