After the recent advances in Artificial Intelligence (AI), and especially in Machine Learning (ML) and Deep Learning (DL)*, various other computer science fields have gone into a race of "blending" their existing methods with ML/DL. There are two directions to enable such a blend: Either using ML to advance the field, or using the methods developed in the field to improve ML.
A commonly used slogan when combining ML with a computer science field is: ML for Xor X for ML, where X can be, for instance, any of {databases, systems, reasoning}. 

In this blog post, we focus on cases where X = big data management. We have already observed works on ML for data management and on data management for ML since several years now. Both directions have a great impact in both academia, with dedicated new conferences popping up, as well as in the industry, with several companies working on either improving their technology with ML or providing scalable and efficient solutions for ML.

Databloom.ai is one of the first companies to embrace both directions within a single product. Within Databloom's product, Blossom, we utilize ML to improve the optimizer and, thus, provide better performance to users but also we utilize well-known big data management techniques to speed up federated learning.