How we built an AI factory — Parts 2&3

How we industrialized deep learning algorithms to production by building a unique agile framework. This series explains how we create super performing models even on premise.

As I explained in Part 1 of this series, most data scientists and communications around AI focus on performances and how to get the best F1 score or mAp. However when developing AI enabled systems (especially on premise like Preligens) performances are key but they are only the tip of the iceberg.

The hardest technical challenge to overcome is to take this performance to production, to iterate fast enough to improve the product with an industrial approach.

Growing from 1 to a 70 people tech team, all working on deep learning and artificial intelligence, in less than 4 years — our first employee arrived in July 2017! — we faced production problems daily.

How to scale teams and technology fast enough ?

How to scale and still improve algorithms and ensure they are reproducible, maintainable and standardized for the whole company and products ?

In this article we will explain how we managed to industrialize the creation of new detectors and made the integration of state of the art models easy to use and deploy even when products are deployed on premise, sometimes even on classified environments.

Our strategy :

• Focus on research and development : A dedicated R&D team to test and integrate state of the…