This section contains ML Packages examples to help you get started building your own.
This is a sample ML Package for the archetypal Iris Flower dataset. The ML Package is retrainable and offers an example on how to split data into training and testing in
process_data. In addition, it offers an example on saving artifacts to pipeline output.
This is a template/boilerplate ML Package. The ML Package has all the functions needed to deploy and train however, it does not do anything functional. It is meant to be informative and can be used as a template to start building your own ML Packages. Like Iris Flower Classifier it shows an example on how to split data and save artifacts. This package also shows one way in which a model with transfer learning might be structured.
This is an example of an ML Package (non-retrainable) for image classification. It is based on the paper "Rethinking the Inception Architecture for Computer Vision" by Szegedy et al.
Updated about a year ago