Deploying a model with containers

In the world of MLOps, containers have become a cornerstone for deploying ML models, offering a lightweight, consistent, and scalable solution for running applications, including ML models, across various environments. Containers encapsulate an application, its dependencies, and runtime into a single package, ensuring that the model behaves the same way regardless of where it is deployed.

This is particularly important in MLOps, where models need to perform consistently across development, testing, and production environments. Once the model is containerized, it can be deployed to a variety of platforms. Cloud services such as Azure Kubernetes Service (AKS) or Amazon Elastic Kubernetes Service (EKS) can be used to manage and scale containers.

Containers address several key challenges in MLOps. First, they solve the “it works on my machine” problem by providing an isolated environment that is consistent across all stages of the deployment...