You're reading from Engineering MLOps

Product typeBook

Published inApr 2021

PublisherPackt

ISBN-139781800562882

Edition1st Edition

Tools

Azure Functions

Concepts

Machine Learning

Author (1)

Emmanuel Raj

Exploring different modes of serving ML models

In this section, we will consider how a model can be served for users (both humans and machines) to consume the ML service efficiently. Model serving is a critical area, which an ML system needs to succeed at to fulfill its business impact, as any lag or bug in this area can be costly in terms of serving users. Robustness, availability, and convenience are key factors to keep in mind while serving ML models. Let's take a look at some ways in which ML models can be served: this can be via batch service or on-demand mode (for instance, when a query is made on demand in order to get a prediction). A model can be served to either a machine or a human user in on-demand mode. Here is an example of serving a model to a user:

Figure 12.2 – Serving a model to users

In a typical scenario (in on-demand mode), a model is served as a service for users to consume, as shown in Figure 12.2. Then, an external application...

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Engineering MLOps

Published in: Apr 2021Publisher: PacktISBN-13: 9781800562882

Author (1)

Emmanuel Raj

Emmanuel Raj is a Finland-based Senior Machine Learning Engineer with 6+ years of industry experience. He is also a Machine Learning Engineer at TietoEvry and a Member of the European AI Alliance at the European Commission. He is passionate about democratizing AI and bringing research and academia to industry. He holds a Master of Engineering degree in Big Data Analytics from Arcada University of Applied Sciences. He has a keen interest in R&D in technologies such as Edge AI, Blockchain, NLP, MLOps and Robotics. He believes "the best way to learn is to teach", he is passionate about sharing and learning new technologies with others.
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