Deep learning driven by large neural network models is overtaking traditional
machine learning methods for understanding unstructured and perceptual data
domains such as speech, text, and vision. At the same time, the
"as-a-Service"-based business model on the cloud is fundamentally transforming
the information technology industry. These two trends: deep learning, and
"as-a-service" are colliding to give rise to a new business model for cognitive
application delivery: deep learning as a service in the cloud. In this paper,
we will discuss the details of the software architecture behind IBM's deep
learning as a service (DLaaS). DLaaS provides developers the flexibility to use
popular deep learning libraries such as Caffe, Torch and TensorFlow, in the
cloud in a scalable and resilient manner with minimal effort. The platform uses
a distribution and orchestration layer that facilitates learning from a large
amount of data in a reasonable amount of time across compute nodes. A resource
provisioning layer enables flexible job management on heterogeneous resources,
such as graphics processing units (GPUs) and central processing units (CPUs),
in an infrastructure as a service (IaaS) cloud.

本文介绍了 IBM 的深度学习服务（DLaaS）的软件架构细节，该服务提供深度学习库，通过分布和编排层，资源供应层实现在云中可伸缩和弹性的管理，利用深度学习以及云计算相结合的业务模式，将传统机器学习方法向深度学习推进。