ML Model Deployment: What is it?

Model Deployment means to integrate a machine learning model and integrate it into an existing production environment where it can take in an input and return an output. (A production environment the setting where software and other products are put into operation for their intended uses by end users)

We want a system that can predict from a trained ML model. Pushing the ML model to such a system is its deployment.

Model deployment is closely related to ML systems architecture. So before we deploy a model, our machine learning model needs to fulfill a couple of criteria before it’s ready for deployment:

• Portability: this refers to the ability of your software to be transferred from one machine or system to another. A portable model is one with a relatively low response time and one that can be rewritten with minimal effort.
• Scalability: this refers to how large your model can scale. A scalable model is one that doesn’t need to be redesigned to maintain its performance.

High-Level Architecture of an ML System

At a high-level, there are four main parts to an ML system:

1. Data Layer: the data layer provides access to all of the data sources…