Keep up to date with the latest developments in trusted AI, MLOps and their applications in your sector.
News in the spotlight
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.
All our news
The industrialisation of AI & the concept of MLOps
MLOps appears to be a necessity to overcome the difficulties in scaling up AI within companies: reproducibility, versioning, continuous integration... This was the subject of one of the conferences on the industrialisation of artificial intelligence at Enjeu Day Industrie & Services 2022. You couldn't attend? Watch the replay.
Implement PostgreSQL Pool connection in Rust
At Craft AI, we build a new product so data scientists can code and push, quickly and easily, in production, their machine learning algorithms. Our purpose is to make life easier for data scientists, for example, we handle data storage in a nice way so data scientists do not have to bother with saving and loading data from a database.
The opportunities for using AI in retail
Artificial intelligence (AI) has received renewed media attention in recent years. AI, a technology of general utility applicable in all areas of society, suggests considerable economic and social impacts
Immersion in prehistory thanks to AI
To be able to walk through a cave inhabited more than 500,000 years ago, to visualise the remains of a human occupation and then to go out into the valley that borders it to observe the fauna and flora of that period: this is the dream of the archaeologists of the Tautavel site in the Pyrénées-Orientales.
Cybersecurity: Astek and Craft AI join forces
Astek and Craft AI, engaged in a co-development partnership of machine learning solutions, present their first achievement in the field of cybersecurity. The 2 companies have co-developed a continuous authentication system based on the behavioural analysis of computer system users.
Personalized virtual coach with Machine Learning
Imagine a fitness app where a virtual assistant helps users to reach their goals and to stay motivated. For the assistant to be engaging, it must give actionable and personalized advice, and adjust the plan to the actual progress of the user. In other words, a good coach must know its coachee.
Predictive UX boosts your website conversion rate
As many web-based marketplaces, banks rely on their website and mobile app to propose products like loans and insurances to the users. The conversion rate, that is to say the percentage of visitors that purchase a product, is often pretty low. The reasons are multiple: a great diversity of offers, non-targeted propositions, doubtful users... This article presents how Craft AI boosts a website conversion rate by proposing adapted products to the users using predictive UX and hyper-personalized content.
Automated periodicity detection in time series
We recently presented a scientific paper during ECML PKDD Advanced Analytics and Learning on Temporal Data workshop part of the ECML-PKDD 2019 conference at Wurzburg, Germany.