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As machine learning is applied more widely, data scientists often struggle tofind or create end-to-end machine learning systems for specific tasks. Theproliferation of libraries and frameworks and the complexity of the tasks haveled to the emergence of "pipeline jungles" - brittle, ad hoc ML systems. Toaddress these problems, we introduce the Machine Learning Bazaar, a newframework for developing machine learning and automated machine learningsoftware systems. First, we introduce ML primitives, a unified API andspecification for data processing and ML components from different softwarelibraries. Next, we compose primitives into usable ML pipelines, abstractingaway glue code, data flow, and data storage. We further pair these pipelineswith a hierarchy of AutoML strategies - Bayesian optimization and banditlearning. We use these components to create a general-purpose, multi-task,end-to-end AutoML system that provides solutions to a variety of datamodalities (image, text, graph, tabular, relational, etc.) and problem types(classification, regression, anomaly detection, graph matching, etc.). Wedemonstrate 5 real-world use cases and 2 case studies of our approach. Finally,we present an evaluation suite of 456 real-world ML tasks and describe thecharacteristics of 2.5 million pipelines searched over this task suite.

The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development