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Serverless Machine Learning in Action: Costs

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Good day,

If you are a small company, should serverless ML be avoided due to costs that it might incur? How should you approach serverless ML if you have limited resources?

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Hi Jose,

Thank you for your question. In general, if you are cost-constrained, then cloud is a double edged sword. For a small business it might be more simple to take care of a well known fixed capital expense upfront (for example buy a few servers) than to subscribe to a cloud service where the total cost of the IT resources is less clear upfront. I find that many small businesses have figured out how to limit their cloud service subscription expenses or how to take advantage of the numerous credits or promotions from major cloud providers (AWS, Azure, GCP) so that subscription cost is less of an issue.

With that said, the point of serverless ML is to reduce OPERATIONAL costs. In other words, if you are a small business and you don't have the money to hire administrators or SREs to babysit your machine learning system in production, then serverless ML is the right approach for you. With serverless ML, you design your ML system so that in production you have little to no need for operations personnel. Hence, you can take your ML expertise, apply it to the serverless ML design, get to the market sooner, and scale up or down to keep the operational costs in-line with the demand for your ML system.
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