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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 of this repository 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 scoring service or scoring agent listening to real time events.

Run local

For development purpose, you can also run kafka, zookeeper and postgresql and the solution on your laptop. For that read this readme.

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.