  This paper overviews an approach that addresses machine learning over
relational data as a database problem. This is justified by two observations.
First, the input to the learning task is commonly the result of a feature
extraction query over the relational data. Second, the learning task requires
the computation of group-by aggregates.
  This approach has been already investigated for a number of supervised and
unsupervised learning tasks, including: ridge linear regression, factorisation
machines, support vector machines, decision trees, principal component
analysis, and k-means; and also for linear algebra over data matrices.
  The main message of this work is that the runtime performance of machine
learning can be dramatically boosted by a toolbox of techniques that exploit
the knowledge of the underlying data. This includes theoretical development on
the algebraic, combinatorial, and statistical structure of relational data
processing and systems development on code specialisation, low-level
computation sharing, and parallelisation. These techniques aim at lowering both
the complexity and the constant factors of the learning time.
  This work is the outcome of extensive collaboration of the author with
colleagues from RelationalAI, in particular Mahmoud Abo Khamis, Molham Aref,
Hung Ngo, and XuanLong Nguyen, and from the FDB research project, in particular
Ahmet Kara, Milos Nikolic, Maximilian Schleich, Amir Shaikhha, Jakub Zavodny,
and Haozhe Zhang. The author would also like to thank the members of the FDB
project for the figures and examples used in this paper.
  The author is grateful for support from industry: Amazon Web Services,
Google, Infor, LogicBlox, Microsoft Azure, RelationalAI; and from the funding
agencies EPSRC and ERC. This project has received funding from the European
Union's Horizon 2020 research and innovation programme under grant agreement No
682588.
