Data and AI Reference Architecture

In this reference architecture, we are focusing on defining architecture patterns and best practices to build data and AI intensive applications. We are addressing how to integrate data governance, machine learning practices and the full life cycle of a cloud native solution development under the same reference architecture to present and holistic point of view on how to do it.

We propose you navigate the content using the book format to have the full explanation of the following reference architecture

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.

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

Pushing the book to GitHub Pages

Ensure that all your local changes to the master branch have been committed and pushed to the remote repository.
1. git push origin master
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
```
Run mkdocs gh-deploy from the root refarch-da directory.

Contribute

We welcome your contributions. There are multiple ways to contribute: report bugs and improvement suggestion, improve documentation and contribute code. We really value contributions and to maximize the impact of code contributions we request that any contributions follow these guidelines:

The contributing guidelines are in this note.

Contributors

Project Status

05/21/19 Starting 11/11/19 evolution in methodology and architecture practices