At OVH, our innovation process stimulates internal collaboration. We have seen this with frugal innovation, but also externally with SMEs, schools or laboratories. An example of collaboration with academics is a particular thesis launched this year, about Artificial Intelligence, which focused on Automated Machine Learning field.
Collaborative research at OVH through a thesis
A CIFRE thesis means, in French, Convention Industrielle de Formation par la Recherche, literally Industrial Convention for Training through Research. The aim is to encourage research collaboration between private and public entities. For enterprise and PhD students, CIFRE is a means of training through research and acquiring strong scientific expertise. For academics, it is a means of managing a new PhD and of applying research results on an economic case. ANRT association manage the CIFRE thesis.
OVH and ORKAD, a research team specialized in combinatorial optimization and knowledge extraction launched a collaboration through a CIFRE thesis.
AutoML, as an example of an AI thesis
The thesis is entitled “MO-AutoML: a multiobjective framework to automatically configure Machine Learning pipelines“. MO stands for Multi-Objective and AutoML for Automated Machine Learning.
Machine Learning is a field of artificial intelligence used for a wide scope of applications like health prediction, shape recognition in embedded systems (e.g., autonomous car), marketing strategy selection, anomaly detection (e.g., temperature in a datacentre). Machine Learning algorithms are very efficient at exploiting data and extracting knowledge used to support decisions.
The main problem with these algorithms, is the technical challenge involved in selecting and tuning algorithms for good performance. That’s why the field of AutoML has emerged, in order to tackle this challenge by automatically selecting and optimising the ML algorithm. Also, AutoML aims to automatically solve other problems related to the field of Machine Learning, such as data formatting, explaining the results (e.g., feature importance), industrialising models, and so on.
Another problem with the current AutoML solutions, is that they are mainly single-objective. However, it can be very interesting to take several metrics measuring the quality of the model in addition to exogenous metrics, and let the user select the model in order to better address the basic problem.
This thesis aims to advance the issues mentioned above, thus facilitating and improving the use of AutoML.
OVH and AI
Certainly, the AutoML thesis will have multiple consequences for OVH. From now, our work on Machine Learning has allowed us to launch, Prescience, in our Labs. Prescience is a distributed and scalable platform that allows the user to build, deploy and query ML models.
As a result of strong collaboration with private partner NVIDIA, OVH provides the NVIDIA GPU Cloud (NGC) software platform as a European exclusive. The purpose of this partnership is to facilitate access to artificial intelligence by allowing users to run their processing, through NGC, on NVIDIA products hosted on the OVH infrastructure.