ML platforms help enable intelligent data-driven applications and maintain them with limited engineering effort. Upon sufficiently broad adoption, such platforms reach economies of scale that bring greater component reuse while improving efficiency of system development and maintenance. For an end-to-end ML platform with broad adoption, scaling relies on pervasive ML automation and system integration to reach the quality we term self-serve that we define with ten requirements and six optional capabilities. With this in mind, we identify long-term goals for platform development, discuss related tradeoffs and future work. Our reasoning is illustrated on two commercially-deployed end-to-end ML platforms that host hundreds of real-time use cases -- one general-purpose and one specialized.

翻译：机器学习平台有助于实现智能数据驱动应用，并以有限的工程开销对其进行维护。在达到足够广泛的采用后，此类平台可实现规模经济，在提高系统开发和维护效率的同时，促进组件复用。对于广泛采用的端到端机器学习平台，其可扩展性依赖于普适的机器学习自动化和系统集成，以达到我们定义为"自助服务"的质量水平，我们为此提出了十项要求与六项可选能力。基于此，我们明确了平台开发的长期目标，讨论了相关权衡与未来工作方向。我们的论证通过两个已商业部署且承载数百个实时用例的端到端机器学习平台（一个通用型平台与一个专用型平台）加以阐释。