A data science journey, from notebooks to a deployed product — Part II

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PART II: Deploy and scale

TL;DR

What is it?

At Artefact, we are so French that we have decided to apply Machine Learning to croissants.
In this second article of the series of two, I will dive into the deployment and the maintenance of our models into production. If you missed the first one about data crunching, feature engineering, cannibalization and our favorite model Catboost, here is the link.

We will talk about some best practices in MLOps such as CI/CD, reproducibility, monitoring and maintenance. Finally our choices in terms of pipeline orchestration and the tools we chose within the GCP ecosystem.
This article’s goal is to share an end to end feedback on how we deployed a ML model in production and give you some tips based on real life projects in order to help you to avoid the same mistakes we made and speed up your deployments. …


A data science journey, from notebooks to a deployed product - Part I

Photo from https://www.visit.alsace/
Photo from https://www.visit.alsace/

TL;DR

What is it?

At Artefact, we are so French that we have decided to apply Machine Learning to croissants. This first article out of two explains how we have decided to use Catboost to predict the sales of “viennoiseries”. The most important features driving sales were the last weekly sales, whether the product is in promotion or not and its price. We will present to you some nice feature engineering including cannibalization and why you sometimes need to update your target variable. We chose the Forecast Accuracy and the biais as evaluation metrics. …

About

Pierre-Yves MOUSSET

Senior Data Scientist at Artefact

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