ML End To End

Overview

ML-End-to-End is an End-to-End bare minimum boilerplate Machine Learning application. It performs various steps including loading a toy dataset, dummy preprocess step through a data wrangler middleware, training a model, and saving it to disk. The prediction service uses the data wrangler middleware to preprocess and predict the requests received through a Flask REST API layer.

Features

• Toy dataset: The application loads a toy dataset for training the model.
• Data wrangler middleware: It includes a data wrangler middleware that is consumed by both the training and prediction steps. It preprocesses the data before passing it to the model.
• Model training: The application trains a model and saves it to disk for future use.
• Flask REST API: The prediction service uses a Flask REST API layer to consume the trained model and accept prediction requests.

Summary

ML-End-to-End is a bare minimum boilerplate Machine Learning application that provides an end-to-end solution for training and deploying models. It includes features such as a data wrangler middleware for preprocessing the data, model training, and a Flask REST API for receiving prediction requests. By following the installation guide, users can easily set up and use this application for their Machine Learning projects.