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cd4ml-workshop

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Repository with sample code and instructions for "Continuous Intelligence" and "Continuous Delivery for Machine Learning: CD4ML" workshops

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- .gitignore
- config
- README.md
- - 1-open-terminal.png
  - 1-sample-app.png
  - 3-app-pipeline.png
  - 3-ml-pipeline.png
  - 4-mlflow-setup.png
  - 4-mlflow.png
  - 5-fluentd-setup.png
  - 5-kibana.png
  - gear.png
- 1-setup.md
- 2-deployment-pipeline.md
- 3-machine-learning-pipeline.md
- 4-tracking-experiments.md
- 5-model-monitoring.md
- Exploratory_Analysis.ipynb
- Feature_Engineering.ipynb
- Negative_Sales.ipynb
- README.md
- web.yml
- metrics.json
- __init__.py
- app.py
- decision_tree.py
- download_data.py
- evaluation.py
- splitter.py
- tracking.py
- app_test.py
- evaluation_test.py
- splitter_test.py
- test.py
.gitignore
CODE_OF_CONDUCT.md
deploy.sh
Dockerfile
Dockerfile_cd4ml_setup
LICENSE
README.md
requirements.txt
run_decisiontree_pipeline.sh
setup-git.sh
start.bat
start.sh
Strata-london-2019-slides.pdf
undeploy.sh

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*NOTE: We are archiving this repository, as it's not been maintained and updated recently.
We will keep it read-only for anyone interested in forking and evolving it independently*

This workshop contains the sample application and machine learning code used for
the Continuous Delivery for Machine Learning (CD4ML) and Continuous Intelligence
workshop. This material has been developed and is continuously evolved by
[ThoughtWorks](www.thoughtworks.com/open-source) and has been presented in
conferences such as: Yottabyte 2018, World AI Summit 2018, Strata London 2019,
and others.

## Pre-Requisites

In order to run this workshop, you will need:

* A valid Github account
* A working Docker setup (if running on Windows, make sure to use Linux containers)

## Workshop Instructions

The workshop is divided into several steps, which build on top of each other.
Instructions for each exercise can be found under the
[`instructions`](./instructions) folder.

*WARNING: the exercises build on top of each other, so you will not be able to
skip steps ahead without executing them.*

*WARNING 2: the workshop requires infrastructure that we only provision when
needed, therefore you won't be able to execute the exercises on your own that
require that shared infrastructure. We are working on a setup that allows
running the workshop locally, but that is work in progress.*

## The Machine Learning Problem

We built a simplified solution to a Kaggle problem posted by Corporación Favorita,
a large Ecuadorian-based grocery retailer interested in improving their
[Sales Forecasting](https://www.kaggle.com/c/favorita-grocery-sales-forecasting/overview)
using data. For the purposes of this workshop, we have combined and simplified
their data sets, as our goal is not to find the best predictions, but to
demonstrate how to implement CD4ML.

## Collaborators

The material, ideas, and content developed for this workshop were contributions
from (in alphabetical order):

* [Arif Wider](https://github.com/arifwider)
* [Arun Manivannan](https://github.com/arunma)
* [Christoph Windheuser](https://github.com/ciwin)
* [Danilo Sato](https://github.com/dtsato)
* [Danni Yu](https://github.com/danniyu)
* [David Tan](https://github.com/davified)
* [Emily Grasmeder](https://github.com/emilyagras)
* [Emily Gorcenski](https://github.com/Gorcenski)
* [Jin Yang](https://github.com/yytina)
* [Jonathan Heng](https://github.com/jonheng)