ICSA Faculty Talk - 21/01/2021

Title:

In-Database Machine Learning for End-to-End Data Analytics

Abstract:

We consider the problem of training machine learning models over multi-relational data. The mainstream approach is to first construct the training dataset using a feature extraction query over input database and then use a statistical software package of choice to train the model. In this talk, we introduce Iterative Functional Aggregate Queries (IFAQ), a framework that realizes an alternative approach. IFAQ treats the feature extraction query and the learning task as one program given in the IFAQ's domain-specific language, which captures a subset of Python commonly used in Jupyter notebooks for rapid prototyping of machine learning applications. The program is subject to several layers of IFAQ optimizations, such as algebraic transformations, loop transformations, schema specialization, data layout optimizations, and finally compilation into efficient low-level C++ code specialized for the given workload and data.

Bio:

Amir Shaikhha is a Lecturer in the School of Informatics at the University of Edinburgh. His research focuses on the design and implementation of data-analytics systems by using techniques from the databases, programming languages, compilers, and machine learning communities. Prior to that, he was a Departmental Lecturer at Oxford. He earned his Ph.D. from EPFL in 2018, for which he was awarded a Google Ph.D. Fellowship in structured data analysis, as well as a Ph.D. thesis distinction award.