What is the Difference Between Deploying and Serving an ML Model?

Tiara Williamson
Tiara WilliamsonAnswered

There are two distinct procedures involved in deploying and serving a machine learning (ML) model, despite the fact that these terms are sometimes used interchangeably.

ML model deploying

Often, when people talk about deploying ML models, they mean taking a trained model and packing it up so it can be used in a production setting. Converting the model to a format that is suitable for deployment on a web service, mobile device, or any other kind of platform is one possible step in this process. While deploying a model, it is possible that additional infrastructure, such as servers and databases, would need to be put up to support the model.

ML model serving

On the other hand, serving a machine learning model is the process of making an already deployed model accessible for usage. This is often accomplished by making the model accessible to users through an application programming interface (API) or web service. This enables users to feed the model input data and return predictions or other outputs.

Consider a machine learning model that has been trained to categorize photos as an example to explain the distinction between deploying and servicing an ML model. It’s possible that the process of distributing the model will require converting it to a format that can be distributed to a web service. Doing this step may require establishing infrastructure, such as virtual machines or containers, to host the model and any necessary dependencies.

ML model hosting

In contrast, machine learning model hosting is publishing the trained model to a cloud service where it may be accessed and exploited by many programs. This might entail releasing the model on a cloud service. Load balancing, data storage, and security are only some of the many extra features offered by the hosting platform.

Summary

Installing and servicing a machine learning model are two distinct operations, notwithstanding their tight relationship with one another. When you use deployment methods for ML models, you are preparing them for usage in a production environment. Serving a model means making the deployed model accessible to users via an API or a web service.

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