The boom of deep learning induced many industries and academies to introduce
machine learning based approaches into their concern, competitively. However,
existing machine learning frameworks are limited to sufficiently fulfill the
collaboration and management for both data and models. We proposed NSML, a
machine learning as a service (MLaaS) platform, to meet these demands. NSML
helps machine learning work be easily launched on a NSML cluster and provides a
collaborative environment which can afford development at enterprise scale.
Finally, NSML users can deploy their own commercial services with NSML cluster.
In addition, NSML furnishes convenient visualization tools which assist the
users in analyzing their work. To verify the usefulness and accessibility of
NSML, we performed some experiments with common examples. Furthermore, we
examined the collaborative advantages of NSML through three competitions with
real-world use cases.

我们提出了 NSML，一种机器学习即服务（MLaaS）平台，以满足在企业规模下开发机器学习工作的需求，为用户提供了易于使用的集群以及协作环境，同时提供可视化工具以协助用户分析其工作。通过实验证明了 NSML 的有用性和可访问性，并通过三个具有真实使用案例的竞赛检验了 NSML 的协作优势。

NSML：遇见具有真实世界案例的 MLaaS 平台

NSML: Meet the MLaaS platform with a real-world case study

Machine learning libraries such as TensorFlow and PyTorch simplify model
implementation. However, researchers are still required to perform a
non-trivial amount of manual tasks such as GPU allocation, training status
tracking, and comparison of models with different hyperparameter settings. We
propose a system to handle these tasks and help researchers focus on models. We
present the requirements of the system based on a collection of discussions
from an online study group comprising 25k members. These include automatic GPU
allocation, learning status visualization, handling model parameter snapshots
as well as hyperparameter modification during learning, and comparison of
performance metrics between models via a leaderboard. We describe the system
architecture that fulfills these requirements and present a proof-of-concept
implementation, NAVER Smart Machine Learning (NSML). We test the system and
confirm substantial efficiency improvements for model development.

我们提出了一个名为 NAVER Smart Machine Learning (NSML) 的系统，用于处理机器学习任务中的 GPU 分配、可视化学习状态、处理模型参数快照、超参数修改以及性能指标比较等任务，通过实验确认其能够提升模型开发效率。