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 art research into our models, in test but also in production
- Create a unique framework : our AI factory : A toolbox for our AI teams, helping them create in a short period of time high accuracy and reliable detectors, to industrialize AI algorithms creation.
- Dedicate a team to this framework : our AI engineering team. A third of our AI engineers are working on the framework. This is a multidisciplinary team, from Machine Learning engineers, to computer vision researchers, architects, fullstack developers and devOps.
Our AI factory makes it possible to :
Part 1 — Scale our company ensuring quality and reproducibility of our detectors: More than a dozen people are working on the framework full time with an expected growth of 60% in 2021.
Part 2 — Create the best possible performing models : Never less than 95% and up to 98% accuracy with State of the Art model…