Why package ML models?

MLOps enables a systematic approach to train and evaluate models. After models are trained and evaluated, the next steps are to bring them to production. As we know, ML doesn't work like traditional software engineering, which is deterministic in nature and where a piece of code or module is imported into the existing system and it works. Engineering ML solutions is non-deterministic and involves serving ML models to make predictions or analyze data.

In order to serve the models, they need to be packed into software artifacts to be shipped to the testing or production environments. Usually, these software artifacts are packaged into a file or a bunch of files or containers. This allows the software to be environment- and deployment-agnostic. ML models need to be packaged for the following reasons:

Portability

Packaging ML models into software artifacts enables them to be shipped or transported from one environment to another. This can be done...