Restez informés des dernières évolutions de l’IA de confiance, du MLOps et de ses applications dans votre secteur.
Les actus à la une
How MLOps will streamline your AI projects?
When speaking of Artificial Intelligence, the efficiency and profitability of projects depend on the ability of companies to deploy reliable applications quickly and at low cost. To succeed, you need to organize and improve the processes for creating, implementing, and maintaining AI models with a diverse and sizable team.
Don’t just build models, deploy them too!
You don’t know what “model deployment” means? Even when you try to understand what it means, you end up searching for the meaning of too many baffling tech words like “CI/CD”, “REST HTTPS API”, “Kubernetes clusters”, “WSGI servers”… and you feel overwhelmed or discouraged by this pile of new concepts?
Un-risk Model Deployment with Differential Privacy
As a general rule, all data ought to be treated as confidential by default. Machine learning models, if not properly designed, can inadvertently expose elements of the training set, which can have significant privacy implications. Differential privacy, a mathematical framework, enables data scientists to measure the privacy leakage of an algorithm. However, it is important to note that differential privacy necessitates a tradeoff between a model's privacy and its utility. In the context of deep learning there are available algorithms which achieve differential privacy. Various libraries exist, making it possible to attain differential privacy with minimal modifications to a model.
Toutes nos actualités
How operationalizing XAI enables stock management?
Overstocks, out-of-stocks, dead stock … Retailers’ business depends on their ability to optimize their warehouse management. A lot of progress has been made in the last decade thanks to new management paradigms and control processes.
How eXplainable AI improves energy performance
Due to rising energy cost, scarcity of resources and the need to limit environmental impacts, Dalkia has decided to put the optimization of energy performance at the heart of its strategy. To meet this challenge, Dalkia created the Desc (Dalkia Energy Savings Center) back in 2013, a control center for energy performance management of its customers’ sites to ensure better comfort and lower energy consumption.
Information gain ratio correction
This paper presents an improvement of the information gain function used in a lot of decision tree Machine Learning algorithms. It was published on arXiv.org.
Periodic split method
This paper presents specific features of the Craft AI Machine Learning engine that enable it to better take into account typical rhythms in human activities. In particular, it improves the quality and explainability of predictive models related to those.
Forgetting Methods for White Box Learning
This paper presents some of the foundations of Craft AI and especially how we introduced Machine Learning of user habits in an explainable context. It also introduces the initial version of our forgetting method that is able to unlearn lost habits.