The objective of a Build mission is to take the algorithm out of its machine and push it into the production environment. We can then monitor the model's performance in real-life conditions and adapt it thanks to users' feedback. At the end of a Build mission, the client is in possession of an AI solution used daily in production.
For a Build mission, a technical team is dedicated a 100% of its time to the project. The team is made of a Lead Data Scientist, two Data Scientists, an Agile Coach, and a Project Director. The two factors necessary to the successful development of an AI product are: 1. The involvement of a technical expert from the industry who can bring his knowledge to the development of the solution. They are integrated in our teams for 30% of their time. 2. The continuous enrichiment of a dataset to improve the accuracy of our models.
At the end of a Build mission, the client is the owner of the developed code. The product is used in conditions and the API is ready to be deployed in more geographic sites or to have its functional scope be extended. A few Build missions we completed: 1. A food recognition solution installed in restaurants for automatic billing. 2. Division by 30 of the time necessary to create an installation diagram.
How is predictive maintenance going the revolutionize industry? (In French)
According to a McKinsey study, predictive maintenance will allow companies to save up to $630 Billions by 2025.
Artificial Intelligence: The End of Law Firms? (In French)
The next Silicon Valley unicorn will likely be a law firm.
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Discover what the main challenges you might face in this critical phase are
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