Why using Docker for Machine Learning ?

Why using Docker for Machine Learning ?

In this tutorial, we will learn why to use Docker containers for Machine Learning. Learn how to resolve the “it works in my machine” problem.

The first thing to understand before talking about containerization is the concept of micro-services. If a large application is broken down into smaller services, each of those services or small processes can be termed micro-services and they communicate with each other over a network. The microservices approach is the opposite of the monolithic approach which can be difficult to scale. If one particular feature has some issues or crashes, all other features will experience the same. Another example is when the demand for a particular feature is seriously increasing, we are forced to increase the resources such as the hardware not only for this particular feature but for the entire application generating additional costs that are not necessary. This cost can be minimized if a micro-services approach is taken by breaking down the application into a group of smaller services. Each service or features of the application is isolated in a way that we can scale or update without impacting other application features. To put machine learning into production, let’s consider that the application needs to be broken down into smaller micro-services such as ingestion, preparation, combination, separation, training, evaluation, inference, postprocessing and monitoring.


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