2023-05-242023-05-242023-052023-02-22May 2023Jankov, Dimitrije. "Declarative Relational Machine Learning Systems." (2023) Diss., Rice University. <a href="https://hdl.handle.net/1911/114887">https://hdl.handle.net/1911/114887</a>.https://hdl.handle.net/1911/114887Several systems, most notably TensorFlow and PyTorch, have revolutionized how we practice machine learning (ML). They allow an ML practitioner to create complex models with great ease. In recent years there has been an explosion in the size of ML models, and it has become apparent that the systems we use today limit the data scientist to a few standard implementations like data parallelism (DP). In an ideal scenario, the ML practitioner would specify their model, and a system would take care of managing the specifics of the computations. My research explores how we can design and implement such systems. Specifically, it tries to find the right set of changes to a declarative relational system so that it can accommodate the needs of ML systems. The results of my research show that one can create scalable distributed machine learning systems that do not constrain the abilities of data scientists and enable greater productivity.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.RelationalSystemsMachine LearningDeclarativeThesis2023-05-24