Repository files navigation

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 forthe Continuous Delivery for Machine Learning (CD4ML) and Continuous Intelligenceworkshop. This material has been developed and is continuously evolved byThoughtWorks and has been presented inconferences such as: Yottabyte 2018, World AI Summit 2018, Strata London 2019,and others.

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)

The workshop is divided into several steps, which build on top of each other.Instructions for each exercise can be found under theinstructions folder.

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

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

We built a simplified solution to a Kaggle problem posted by Corporación Favorita,a large Ecuadorian-based grocery retailer interested in improving theirSales Forecastingusing data. For the purposes of this workshop, we have combined and simplifiedtheir data sets, as our goal is not to find the best predictions, but todemonstrate how to implement CD4ML.

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

• Arif Wider
• Arun Manivannan
• Christoph Windheuser
• Danilo Sato
• Danni Yu
• David Tan
• Emily Grasmeder
• Emily Gorcenski
• Jin Yang
• Jonathan Heng