SystemML: Declarative Machine Learning on Spark

By Matthias Boehm, Michael W. Dusenberry, D. Eriksson, Alexandre V. Evfimievski, F. Manshadi, Niketan Pansare, Berthold Reinwald, Frederick Reiss, Prithviraj Sen, Arvind Surve and 1 more. 2016

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Automated Machine Learning: State-of-The-Art and Open Challenges (2019, background): “Examples include Spark MLib [83], Mahout(33) and SystemML [16].”
Enabling and Optimizing Non-linear Feature Interactions in Factorized Linear Algebra (2019, background): “In particular, SystemML has extensive logical and physical optimizations for LA workloads [8, 9].”
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice (2020, background): “ sklearn and Keras, which restricts them to only work with a single node instead of large data volumes [3]. To tackle large datasets, some distributed machine learning platforms, like Apache SystemML [129] and Spark MLib [130], have been developed; however, only very few HPO frameworks exist that su”
Towards Linear Algebra over Normalized Data (2016, methodology): “Many popular ML algorithms such as linear regression, logistic regression, and K-Means clustering can be expressed succinctly using LA operators such as matrix multiplication and inversion [9].”