What Is Docker Machine Learning

Docker allows to easily reproduce the working environment that is used to train and run the machine learning model anywhere. Docker allows packaging the code and dependencies into containers that can be ported to different servers even if it’s a different hardware or operating system.

What exactly is Docker for?

Docker is an open source containerization platform. It enables developers to package applications into containers—standardized executable components combining application source code with the operating system (OS) libraries and dependencies required to run that code in any environment.

How do you Dockerize a ML model?

1. Train and save your model.
2. Create an API to send data and make predictions with your model.
3. Create a Dockerfile with optional Docker-Compose file specified to your model and it’s requirements.
4. Create and test a container of your model and API.
5. Deploy to application hosting service.