How to package ML models
ML models can be packaged in various ways depending on business and tech requirements and as per operations for ML. ML models can be packaged and shipped in three ways, as discussed in the following sub-sections.
Serialized files
Serialization is a vital process for packaging an ML model as it enables model portability, interoperability, and model inference. Serialization is the method of converting an object or a data structure (for example, variables, arrays, and tuples) into a storable artefact, for example, into a file or a memory buffer that can be transported or transmitted (across computer networks). The main purpose of serialization is to reconstruct the serialized file into its previous data structure (for example, a serialized file into an ML model variable) in a different environment. This way, a newly trained ML model can be serialized into a file and exported into a new environment where it can de-serialized back into an ML model variable...
