Bighead: A FrameworkAgnostic, EndtoEnd 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 frameworkagnostic, endtoend platform for machine learning. It offers a seamless user experience requiring only minimal efforts that span feature set management, prototyping, training, batch (offline) inference, realtime (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 generalpurpose 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 cuttingedge 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.

An Empirical Study Towards Characterizing Deep Learning Development and Deployment Across Different Frameworks and Platforms Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks and platforms play a key role to catalyze such progress. However, the differences in architecture designs and implementations of existing frameworks and platforms bring new challenges for DL software development and deployment. Till now, there is no study on how various mainstream frameworks and platforms influence both DL software development and deployment in practice. To fill this gap, we take the first step towards understanding how the most widelyused DL frameworks and platforms support the DL software development and deployment. We conduct a systematic study on these frameworks and platforms by using two types of DNN architectures and three popular datasets. (1) For development process, we investigate the prediction accuracy under the same runtime training configuration or same model weightsbiases. We also study the adversarial robustness of trained models by leveraging the existing adversarial attack techniques. The experimental results show that the computing differences across frameworks could result in an obvious prediction accuracy decline, which should draw the attention of DL developers. (2) For deployment process, we investigate the prediction accuracy and performance (refers to time cost and memory consumption) when the trained models are migratedquantized from PC to real mobile devices and web browsers. The DL platform study unveils that the migration and quantization still suffer from compatibility and reliability issues. Meanwhile, we find several DL software bugs by using the results as a benchmark. We further validate the results through bug confirmation from stakeholders and industrial positive feedback to highlight the implications of our study. Through our study, we summarize practical guidelines, identify challenges and pinpoint new research directions, such as understanding the characteristics of DL frameworks and platforms, avoiding compatibility and reliability issues, detecting DL software bugs, and reducing time cost and memory consumption towards developing and deploying high quality DL systems effectively.