to full scale production
The MLOps Platform
Develop, deploy and pilot in production your Machine Learning models in record time, without DevOps or ML Engineer. Bring MLOps into your company to accelerate and make your AI projects more reliable.
Develop, deploy and pilot your Machine Learning models in production in record time, without DevOps or ML Engineer.
MLOps for AI
Deploy your models on a large scale
Execute your Pipelines and track each run. Deploy your models, make their results available and set up re-training conditions. Have dedicated working environments for experimentation, testing and production. Choose the exact size of your infrastructure and manage the cost of each project.
A platform compatible with the entire ecosystem
Recommend to students the most appropriate learning content for their learning profile
Develop a trustworthy AI
44% of companies see "black box" AI as a major risk
The State of AI 2021 - MCKinsey
Mouhoud El Mouhoub
President @Université Paris-Dauphine
"Craft AI offers the clearest AI solution for education that is tailored to our needs and is already a true technology success story."
CEO @EDTech France
"Trusted AI at the service of students, explainable and fair, complementing teachers is the beautiful promise of Craft AI."
CDO @Total Direct Energie
"The most remarkable thing is that thanks to Craft.AI's platform, we have developed an assistant that reduces our customers' consumption by an average of 5%!"
MLOps ROI for companies
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.