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Machine Learning Engineering on AWS
book

Machine Learning Engineering on AWS

by Joshua Arvin Lat
October 2022
Intermediate to advanced content levelIntermediate to advanced
530 pages
11h 57m
English
Packt Publishing
Content preview from Machine Learning Engineering on AWS

7

SageMaker Deployment Solutions

After training our machine learning (ML) model, we can proceed with deploying it to a web API. This API can then be invoked by other applications (for example, a mobile application) to perform a “prediction” or inference. For example, the ML model we trained in Chapter 1, Introduction to ML Engineering on AWS, can be deployed to a web API and then be used to predict the likelihood of customers canceling their reservations or not, given a set of inputs. Deploying the ML model to a web API allows the ML model to be accessible to different applications and systems.

A few years ago, ML practitioners had to spend time building a custom backend API to host and deploy a model from scratch. If you were given this requirement, ...

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Publisher Resources

ISBN: 9781803247595Supplemental Content