The gap between machine learning research and how today's organizations are actually leveraging machine learning can be wide. Enter: the Dataiku AI Lab. Our team of experts builds bridges between machine learning research and practical business applications to accelerate our customers' journey to Everyday AI.
The Dataiku AI Lab is a team of researchers that develops robust and generic state-of-the-art machine learning methods to enable trustworthy, real-life applications. The team has a broad range of interests, from active learning to uncertainty estimation, data shifts to casual ML, and more. Learn about a few of the team’s current projects below.
Machine learning model lifecycles are complex and ever evolving. When data changes or drifts, whether naturally or adversarially, it can impact a model’s performance. We need to not only detect data drift but also estimate model performance drop. Check out Dataiku AI Lab’s latest papers on this topic:
When unlabeled data is abundant but labeling resources are limited, active learning leverages knowledge from the model to select samples that maximize the model performance. Dataiku AI Lab has published the following papers on active learning:
We also open source our Python package, Cardinal.
For the past five years, Michelin has been working on incorporating more machine learning into its processes for tire design and testing. Léo Dreyfus-Schmidt, VP of Research at Dataiku and head of the AI Lab, sat down with François Deheeger, Senior Fellow AI and Data Science at Michelin to talk details.
READ ON THE BLOGIn an MIT article, computer science researcher Jeannette Wing says that “Causality … is the next frontier of AI and machine learning.” This technical ebook contains a hearty introduction to causal inference and all its idiosyncrasies, as well as the danger of using regular machine learning to infer causal effects.
GET THE TECHNICAL EBOOKMeasuring similarity between two datasets is critical in many ML fields, such as detecting dataset shift and evaluating its impact on a model’s performance. This article describes various datasets’ similarity measures and how they can be leveraged for distribution shift detection and model performance drop.
READ ON MEDIUMA look into how the R&D team works at Dataiku and what makes it special. TLDR: We prioritize quality of product and people's lives over deadlines.
Engineering at Dataiku
