The Sequence Scope: Serverless ML Execution

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📝 Editorial: Serverless ML Execution

One of the most challenging aspects of modern ML solution is to match the right infrastructure for executing a given ML model. Some are executed continuously while others intermittently. Some models experience periods of high demand and traffic followed by idle times. The point is, that accommodating a single server infrastructure to a variety of ML models is nothing short of a nightmare. The serverless computing paradigm has evolved over the last few years under the premise of executing code functions without the need of pre-provisioning a server infrastructure. Recently, we have seen a number of attempts to adapt serverless computing to ML models. Just this week, we saw one of the biggest announcements in this new ML trend.

Amazon SageMaker Serverless Inference was originally announced at the end of 2021 with the premise of deploying ML models for inference without requiring the provisioning of a server infrastructure. A few days ago, Amazon announced the general availability of this platform…