It is worthwhile comparing the costs for the 3 scenarios without industrialisation with the solution we have planned, and using a Cloud solution.

1. Without industrialisation

In cases where there is no industrialisation, costs are mainly generated by the time wasted by the Data Scientists in their daily tasks:

Manual entry of scores and training metadata
Attempting to reproduce the project baseline or the latest results that match the model currently in production
Testing algorithms that have already been tested before
Manually managing datasets to save them in a shared and backed-up space
Manually re-training models

It’s hard to estimate the number of days lost per year, as this depends on the context and the type of project… however, it is reasonable to assume that it amounts to a major waste of time that should instead have been devoted to tasks that add value: industry watch, model improvement, code improvement, etc.

2. With “in house” industrialisation

The costs associated with the implementation of the aforementioned in-house solution are spread over several items:

An initial cost covering:

Setting up the technical architecture for the solution under OpenShift, which is estimated to take 3 days
Adapting the code from existing projects, which is estimated to take 5 days per project (this can naturally vary from project to project)

Next, the operating costs corresponding to the administration and operation of the solution, which we estimate at 10 days per year, and the costs related to hosting and OpenShift, which we estimate at €1,500 per year.

3. With a Cloud solution

With a Cloud solution, there are (as before) costs associated with adapting the code from existing projects.

Then there are the monthly costs of Machine Learning tools, which vary from one provider to another; taking the example of Azure’s ML Studio, with 4 vCPUs and 16GB of RAM, this comes to $192 per month per Data Scientist, according to https://azure.microsoft.com/en-us/pricing/calculator/ (not including storage, network, etc.).