https://github.com/ibm-cloud-architecture/refarch-reefer-ml

This is a prescriptive maintenance implementation using event-driven architecture 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 maintenance. All run on Openshift.
https://github.com/ibm-cloud-architecture/refarch-reefer-ml

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This is a prescriptive maintenance implementation using event-driven architecture 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 maintenance. All run on Openshift.

• Host: GitHub
• URL: https://github.com/ibm-cloud-architecture/refarch-reefer-ml
• Owner: ibm-cloud-architecture
• Created: 2019-08-26T15:17:24.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-09-30T18:55:01.000Z (over 2 years ago)
• Last Synced: 2023-10-20T20:18:07.775Z (about 1 year ago)
• Topics: eda
• Language: Jupyter Notebook
• Homepage: https://ibm-cloud-architecture.github.io/refarch-reefer-ml
• Size: 82.1 MB
• Stars: 10
• Watchers: 6
• Forks: 19
• Open Issues: 14
• Metadata Files:
  • Readme: README.md

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README

        
# 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](https://ibm-cloud-architecture.github.io/refarch-reefer-ml), 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.

![](docs/images/mvp-runtime.png)

## Run local

For development purpose, you can also run kafka, zookeeper and postgresql and the solution on your laptop. For that read [this readme](./docker/README.md).

## Building this booklet locally

The content of this repository is written with markdown files, packaged with [MkDocs](https://www.mkdocs.org/) and can be built into a book-readable format by MkDocs build processes.

1. Install MkDocs locally following the [official documentation instructions](https://www.mkdocs.org/#installation).

1. Install Material plugin for mkdocs:  `pip install mkdocs-material` 

2. `git clone https://github.com/ibm-cloud-architecture/refarch-reefer-ml.git` _(or your forked repository if you plan to edit)_

3. `cd refarch-reefer-ml`

4. `mkdocs serve`

5. 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.

	```bash

	git checkout gh-pages

	git pull origin gh-pages

	

	git checkout master

	```

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