Machine Learning Inference on Serverless Platforms Using Model Decomposition
Abstract
Serverless offers a scalable and cost-effective service model for users to run applications without focusing on underlying infrastructure or physical servers. While the Serverless architecture is not designed to address the unique challenges posed by resource-intensive workloads, e.g., Machine Learning (ML) tasks, it is highly scalable. Due to the limitations of Serverless function deployment and resource provisioning, the combination of ML and Serverless is a complex undertaking. We tackle this problem through decomposition of large ML models into smaller sub-models, referred to as slices. We set up ML inference tasks using these slices as a Serverless workflow, i.e., sequence of functions. Our experimental evaluations are performed on the Serverless offering by AWS for demonstration purposes, considering an open-source format for ML model representation, Open Neural Network Exchange. Achieved results portray that our decomposition method enables the execution of ML inference tasks on Serverless, regardless of the model size, benefiting from the high scalability of this architecture while lowering the strain on computing resources, such as required run-time memory.
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