Continuous Intelligence and CD4ML Workshop

This workshop contains the sample application and machine learning code used for the Continuous Delivery for Machine Learning (CD4ML) and Continuous Intelligence workshop.

This workshop is based on an existing CD4ML Workshop.

This material has been developed and is continuously evolved by ThoughtWorks and has been presented in conferences such as: ODSC Boston 2020.

Pre-Requisites

In order to run this workshop, you will need:

A valid Github account
A working Docker setup with at least 20 GB of space free (if running on Windows, make sure to use Linux containers)

Tools used in this workshop

Python 3.7
Docker
Jenkins
EFK Stack, ElasticSearch, Fluentd, Kibana
JupyterLab
MLFlow

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 folder. To start from the beginning click here.

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

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 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.

Links to the different components of this scenario

After a successful setup of the environment, the following components are running on your machine:

Collaborators

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