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Declarative machine learning (ML) aims at the high-level specification of MLtasks or algorithms, and automatic generation of optimized execution plans fromthese specifications. The fundamental goal is to simplify the usage and/ordevelopment of ML algorithms, which is especially important in the context oflarge-scale computations. However, ML systems at different abstraction levelshave emerged over time and accordingly there has been a controversy about themeaning of this general definition of declarative ML. Specificationalternatives range from ML algorithms expressed in domain-specific languages(DSLs) with optimization for performance, to ML task (learning problem)specifications with optimization for performance and accuracy. We argue thatthese different types of declarative ML complement each other as they addressdifferent users (data scientists and end users). This paper makes an attempt tocreate a taxonomy for declarative ML, including a definition of essential basicproperties and types of declarative ML. Along the way, we provide insights intoimplications of these properties. We also use this taxonomy to classifyexisting systems. Finally, we draw conclusions on defining appropriatebenchmarks and specification languages for declarative ML.

Declarative Machine Learning - A Classification of Basic Properties and Types