How to deploy machine learning models with AWS Lambda

"In the rapidly evolving landscape of artificial intelligence and machine learning, organizations continue to seek cost-effective solutions to reduce reliance on expensive third-party tools-not only for development but also for deployment. Recently I was tasked with deploying a predictive machine learning (ML) model at my organization. Our original goal was to bring the ML model in-house to reduce operational costs, but the deployment process presented significant challenges due to expensive infrastructure requirements."

"Enter serverless computing, with platforms like AWS Lambda offering a compelling solution for lightweight and on-demand ML inference. The serverless approach is a particularly timely option given the rise in edge computing and machine learning use cases and the need to reduce the excessive costs traditionally associated with ML deployment. In this article, I will walk you through two ways to deploy an ML model on AWS Lambda."

AWS Lambda enables lightweight, on-demand ML inference with a pay-as-you-go serverless model that reduces reliance on expensive licensing and third-party deployment tools. Organizations attempting to bring ML models in-house can avoid heavy infrastructure costs by using serverless compute for inference. For workloads processing roughly 1,000–10,000 predictions per day, serverless deployments can potentially cut infrastructure costs by up to 60% versus dedicated prediction servers. AWS Lambda automatically scales computational resources in response to incoming requests, removing manual provisioning and optimizing resource utilization. The serverless approach aligns with rising edge computing and ML use cases and supports multiple deployment approaches for Lambda-based models.

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