frreiss/reefer

This is a prescriptive maintenance implementation for real time analytics on event stream coming from Reefer Container. The solution include a reefer simulator, a scoring microservice, a container event listener to trigger a business process when the scoring identify the Reefer container needs a maintenance. All run on Openshift.

Reefer Predictive Maintenance Solution

This project presents an approach to develop a predictive maintenance model from Reefer container metrics events and integrate it in real time.

The content is presented in a book view, and the goal is to run all those components together, and build the logistic regression model, deployed as a service.

Building this booklet locally

The content of this repository is written with markdown files, packaged with MkDocs and can be built into a book-readable format by MkDocs build processes.

1. Install MkDocs locally following the official documentation instructions.
2. Install Material plugin for mkdocs: pip install mkdocs-material
3. git clone https://github.com/ibm-cloud-architecture/refarch-reefer-ml.git (or your forked repository if you plan to edit)
4. cd refarch-reefer-ml
5. mkdocs serve
6. Go to http://127.0.0.1:8000/ in your browser.

Pushing the book to GitHub Pages

1. Ensure that all your local changes to the master branch have been committed and pushed to the remote repository.
   1. git push origin master
2. Ensure that you have the latest commits to the gh-pages branch, so you can get others' updates.

   git checkout gh-pages
   git pull origin gh-pages
   
   git checkout master

3. Run mkdocs gh-deploy from the root refarch-reefer-ml directory.