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Modern data analytics workloads combine relational data processing withmachine learning (ML). Most DBMS handle these workloads by offloading these MLoperations to external specialized ML systems. While both DBMS and ML systemsgo to great lengths to optimize performance for their specific workloads,significant performance is lost when used in combination, due to data movementacross system boundaries, conversions between incompatible internal dataformats, and the lack of cross system optimizations.  A key idea to remove these bottlenecks is to integrate existing datamanipulation systems with ML systems by building a common intermediate layer(IR). Although this idea has been explored before (Weld, Delite), previous suchattempts require significant re-engineering of prior systems and still fallshort in achieving best-of-breed performance for individual tasks (e.g., SQL,Deep Learning). Specifically, they rely on re-implementing existing systemsusing a generic set of operators and fail to match best-of-breed individualperformance due to the inability to recover high-level optimizations from thisgeneric IR through compiler analysis.  We present Flern, the first intermediate-layer integration between DB and MLsystems that are best-of-breed individually, competitive with the best compiledquery engines such as HyPer on comprehensive relational benchmarks (TPC-H) andcompetitive with TensorFlow and PyTorch in state-of-the-art ML models (e.g.,DeepSpeech, SqueezeNet, Transformers) and also represents a newstate-of-the-art for integration. A key realization is to architectintermediate layers based on generative programming capabilities, whichpreserves high-level contextual information for cross optimizations and enablesthe construction of a variety of complex structures and cross systemoptimizations with minimal effort.

Architecting Intermediate Layers for Efficient Composition of Data Management and Machine Learning Systems