Declarative Machine Learning - A Classification of Basic Properties and Types
This work addresses the confusion in the ML community about declarative ML definitions, providing a foundational taxonomy for researchers and practitioners, though it is incremental as it organizes existing concepts rather than introducing new methods.
The paper tackles the lack of a clear taxonomy for declarative machine learning, which aims to simplify ML usage through high-level specifications, by proposing a classification of basic properties and types, and uses this taxonomy to categorize existing systems and suggest benchmarks.
Declarative machine learning (ML) aims at the high-level specification of ML tasks or algorithms, and automatic generation of optimized execution plans from these specifications. The fundamental goal is to simplify the usage and/or development of ML algorithms, which is especially important in the context of large-scale computations. However, ML systems at different abstraction levels have emerged over time and accordingly there has been a controversy about the meaning of this general definition of declarative ML. Specification alternatives 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 that these different types of declarative ML complement each other as they address different users (data scientists and end users). This paper makes an attempt to create a taxonomy for declarative ML, including a definition of essential basic properties and types of declarative ML. Along the way, we provide insights into implications of these properties. We also use this taxonomy to classify existing systems. Finally, we draw conclusions on defining appropriate benchmarks and specification languages for declarative ML.