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To support complex data-intensive applications such as personalizedrecommendations, targeted advertising, and intelligent services, the datamanagement community has focused heavily on the design of systems to supporttraining complex models on large datasets. Unfortunately, the design of thesesystems largely ignores a critical component of the overall analytics process:the deployment and serving of models at scale. In this work, we present Velox,a new component of the Berkeley Data Analytics Stack. Velox is a datamanagement system for facilitating the next steps in real-world, large-scaleanalytics pipelines: online model management, maintenance, and serving. Veloxprovides end-user applications and services with a low-latency, intuitiveinterface to models, transforming the raw statistical models currently trainedusing existing offline large-scale compute frameworks into full-blown,end-to-end data products capable of recommending products, targetingadvertisements, and personalizing web content. To provide up-to-date resultsfor these complex models, Velox also facilitates lightweight online modelmaintenance and selection (i.e., dynamic weighting). In this paper, we describethe challenges and architectural considerations required to achieve thisfunctionality, including the abilities to span online and offline systems, toadaptively adjust model materialization strategies, and to exploit inherentstatistical properties such as model error tolerance, all while operating at"Big Data" scale.

The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox