NSML: Meet the MLaaS platform with a real-world case study
October 08, 2018 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Hanjoo Kim, Minkyu Kim, Dongjoo Seo, Jinwoong Kim, Heungseok Park, Soeun Park, Hyunwoo Jo, KyungHyun Kim, Youngil Yang, Youngkwan Kim, Nako Sung, Jung-Woo Ha
arXiv ID
1810.09957
Category
cs.DC: Distributed Computing
Cross-listed
cs.LG,
stat.ML
Citations
137
Venue
arXiv.org
Last Checked
4 months ago
Abstract
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Distributed Computing
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Reproducing GW150914: the first observation of gravitational waves from a binary black hole merger
R.I.P.
π»
Ghosted
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
R.I.P.
π»
Ghosted
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
R.I.P.
π»
Ghosted
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
R.I.P.
π»
Ghosted
iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted