With the growing adoption of Machine Learning (ML) across industries, there is an increasing demand for faster and easier ways to run ML inference at scale. ML use cases, such as manufacturing defect detection, demand forecasting, fraud surveillance, and many others, involve tens or thousands of datasets, including images, videos, files, documents, and other artifacts. These inference use cases typically require the workloads to scale to tens of thousands of parallel processing units. The simplicity and automated scaling offered by AWS serverless solutions makes it a great choice for running ML inference at scale. Using serverless, inferences can be run without provisioning or managing servers and while only paying for the time it takes to run. ML practitioners can easily bring their own ML models and inference code to AWS by using containers.
This post shows you how to run and scale ML inference using AWS serverless solutions: AWS Lambda

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