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.

Avannaldas

ML End To End