Bighead: A Framework-Agnostic, End-to-End Machine Learning Platform

With the increasing need to build systems and products powered by machine learning inside organizations, it is critical to have a platform that provides machine learning practitioners with a unified environment to easily prototype, deploy, and maintain their models at scale. However, due to the diversity of machine learning libraries, the inconsistency between environments, and various scalability requirement, there is no existing work to date that addresses all of these challenges. Here, we introduce Bighead, a framework-agnostic, end-to-end platform for machine learning. It offers a seamless user experience requiring only minimal efforts that span feature set management, prototyping, training, batch (offline) inference, real-time (online) inference, evaluation, and model lifecycle management. In contrast to existing platforms, it is designed to be highly versatile and extensible, and supports all major machine learning frameworks, rather than focusing on one particular framework. It ensures consistency across different environments and stages of the model lifecycle, as well as across data sources and transformations. It scales horizontally and elastically in response to the workload such as dataset size and throughput. Its components include a feature management framework, a model development toolkit, a lifecycle management service with UI, an offline training and inference engine, an online inference service, an interactive prototyping environment, and a Docker image customization tool. It is the first platform to offer a feature management component that is a general-purpose aggregation framework with lambda architecture and temporal joins. Bighead is deployed and widely adopted at Airbnb, and has enabled the data science and engineering teams to develop and deploy machine learning models in a timely and reliable manner. Bighead has shortened the time to deploy a new model from months to days, ensured the stability of the models in production, facilitated adoption of cutting-edge models, and enabled advanced machine learning based product features of the Airbnb platform. We present two use cases of productionizing models of computer vision and natural language processing.