COPML Example 2 - Detecting Pneumonia in Chest X-Rays

This repoistory is used as the second example in the Continuous Operations for Production Machine Learning (COPML) document that provides a framework for maintaning machine learning projects in production. The goal is to build an image classifier model predict the likelihood that a patient has pneumonia based on an image of their chest x-ray. The primary goal of this repository is to build an image classifier that predict whether a patient has pneumonia and if its a viral or bacterial pnemonia from a chest xray image. The project uses tensorflow and transfer learning with MobileNetV2 model, trained on a labled chest x-ray image dataset available on Kaggle.

The aim is to show and end-to-end process of how to build an application in CML that can take a new image and make a prediction on that image in real time use two separate models. The project also uploads the image data to an object store and then pulls the data from that object store during model training. There is also a front-end application to allow users to test and display the model predictions on test images in real time.

Project Structure

The project is organized with the following folder structure:

.
├── app/            # Assets needed to support the front end application
├── code/           # Scripts and files needed to create the various project artifacts
├── data/           # The full image dataset used for model training and other useful data
├── images/         # Images used for the README and documentation
├── notebooks/      # Notebooks used during the model building process 
├── models/         # Directory to hold trained models
├── cdsw-build.sh   # Shell script used to build environment for experiments and models
├── README.md
├── LICENSE.txt
└── requirements.txt

By following the notebooks, scripts, and documentation in the code directory, you will understand how to perform similar tasks on CML, as well as how to use the platform's major features to your advantage.

Deploying on CML

There are three ways to launch the this prototype on CML:

1. From Prototype Catalog - Navigate to the Prototype Catalog on a CML workspace, select the "Airline Delay Prediction" tile, click "Launch as Project", click "Configure Project"
2. As ML Prototype - In a CML workspace, click "New Project", add a Project Name, select "ML Prototype" as the Initial Setup option, copy in the repo URL, click "Create Project", click "Configure Project"
3. Manual Setup - In a CML workspace, click "New Project", add a Project Name, select "Git" as the Initial Setup option, copy in the repo URL, click "Create Project". Then, follow the steps listed in this document in order