The Art and Science of Deploying Machine Learning Models to Production


The Art and Science of Deploying Machine Learning Models to Production Team
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A major issue when working with machine learning models is figuring out how to deploy them to production. It is quite difficult to deploy machine learning models because there are so many different options to work with. The option you choose will have a major impact on how your machine learning models perform in production.

Machine learning model deployment is extra difficult because of how important it is to actually work with new data that comes in. You never know how things will turn out when your machine learning model has to make predictions. When you deploy machine learning model to production, you have to worry about the intricacies that go into working with a production application. That is why model productionalization is such a difficult concept to work with.

Ways to Deploy an ML Model

There are three ways to deploy ML model programs to production. If you know what you’re doing, an ML deployment platform can also be a great choice. Either way, machine learning model deployment happens in multiple ways. You have on-demand prediction services, batch prediction services, and you can even do embedded models that work on smart devices.

These three ways can have a monumental impact on how your machine learning model performs. To deploy machine learning model to production, you have to figure out which one of these ways will be the best for your situation. You have to factor in the type of program you have, CPU power, and environments you are working with. All of these have an impact on how things go.

Deploying as a Web Service

When you want to deploy machine learning model to production as a web service, it is crucial to understand the various frameworks you can use. For example, the Flask framework is a great choice that many people choose. This Framework has many libraries you can integrate to have a web service that acts as an API layer for your application.

This layer receives API calls and returns a result based on the machine learning processing. This is the easiest way to do it, and it offers scalability. This is also the way that is recommended by people who know what they’re doing when it comes to machine learning models.

Deploying with Batch Prediction

Batch prediction is another major way to deploy machine learning model to production. Batch prediction is great because it offers you the chance to scale infinitely. Since you are no longer worrying about making a prediction in real-time, you can batch it into processes and scale the computing power you need. You don’t have to worry about how long it will take, which gives you the chance to simply pass it off to a computing platform that is the cheapest.

You can even distribute it in that way, which is a great choice. You also have the choice of going with edge services, as they are a great way for you to do machine learning on embedded devices. However, the overwhelming majority of people in the industry will never have to work with this type of application. Either way, machine learning models need a lot to make it work in this current paradigm.

About the Author Team Enterprise AI/ML Application Lifecycle Management Platform