Developing, scaling, and deploying modern Machine Learning solutions remains challenging for small- and middle-sized enterprises (SMEs). This is due to a high entry barrier of building and maintaining a dedicated IT team as well as the difficulties of real-world data (RWD) compared to standard benchmark data. To address this challenge, we discuss the implementation and concepts of Chameleon, a semi-AutoML framework. The goal of Chameleon is fast and scalable development and deployment of production-ready machine learning systems into the workflow of SMEs. We first discuss the RWD challenges faced by SMEs. After, we outline the central part of the framework which is a model and loss-function zoo with RWD-relevant defaults. Subsequently, we present how one can use a templatable framework in order to automate the experiment iteration cycle, as well as close the gap between development and deployment. Finally, we touch on our testing framework component allowing us to investigate common model failure modes and support best practices of model deployment governance.
翻译:开发、推广和部署现代机器学习解决方案对于中小企业来说仍然具有挑战性,这是因为建设和维持专门的信息技术团队的进入障碍很大,而且与标准基准数据相比,现实世界数据(RWD)存在困难。为了应对这一挑战,我们讨论了半自动框架Chameleon的落实和概念。Chameleon的目标是快速和可扩展地开发和将适合生产的机器学习系统部署到中小企业的工作流程中。我们首先讨论了中小企业面临的RWD挑战。随后,我们概述了框架的核心部分,即一个模型和损失功能动物园,与RWD相关的默认。随后,我们介绍了如何利用一个可调试的框架使实验循环周期自动化,以及缩小开发和部署之间的差距。最后,我们谈谈我们的测试框架组成部分,使我们能够调查共同的模式失败模式并支持模式部署治理的最佳做法。