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

By Hanjoo Kim, Minkyu Kim, Dongjoo Seo, Jinwoong Kim, Heungseok Park, Soeun Park, Hyunwoo Jo, KyungHyun Kim, Youngil Yang, Youngkwan Kim and 2 more. 2018

137 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems and Management, Management Science and Operations Research, Evaluation & Benchmarks

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Citations in the corpus

CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features (2019, methodology): “All experiments were implemented and evaluated on NAVER Smart Machine Learning (NSML) [19] platform with PyTorch [28].”
Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries (2022, background): “All authors thank NAVER Smart Machine Learning (NSML) [18] for experimental environments.”
Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference (2021, methodology): “All experiments were performed based on NAVER Smart Machine Learning (NSML) platform (Kim et al., 2018; Sung et al., 2017).”
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning (2021, methodology): “The experiments were implemented in PyTorch [26] 1.7 with CUDA 10.1 and CuDNN 7 using two NVIDIA V100 GPUs, and all experiments were conducted with NSML [15] framework.”
Rainbow Memory: Continual Learning with a Memory of Diverse Samples (2021, background): “NAVER Smart Machine Learning (NSML) [25] team for GPU support.”
SWAD: Domain Generalization by Seeking Flat Minima (2021, methodology): “NAVER Smart Machine Learning (NSML) [62] and Kakao Brain Cloud platform have been used in experiments.”
Parallel Wavegan: A Fast Waveform Generation Model Based on Generative Adversarial Networks with Multi-Resolution Spectrogram (2019, methodology): “Experiments were conducted on the NAVER smart machine learning (NSML) platform [29].”
Probability density distillation with generative adversarial networks for high-quality parallel waveform generation (2019, methodology): “All the models and experiments were implemented using NAVER smart machine learning (NSML) platform [29].”
Mixture Content Selection for Diverse Sequence Generation (2019, methodology): “All models are implemented in PyTorch (Paszke et al., 2017) and trained on single Tesla P40 GPU, based on NAVER Smart Machine Learning (NSML) platform (Kim et al., 2018a).”
Probabilistic Embeddings for Cross-Modal Retrieval (2021, methodology): “All experiments were conducted on NAVER Smart Machine Learning (NSML) [19] platform.”
Character Region Awareness for Text Detection (2019, methodology): “All experiments are performed with NAVER Smart Machinie Learning (NSML) platform [14, 34].”
Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels (2021, unknown): “NAVER Smart Machine Learning (NSML) [23] has been used for experiments.”
A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization (2019, methodology): “All computations were done on NAVER Smart Machine Learning (NSML) platform (Sung et al., 2017; Kim et al., 2018).”
Reward Optimization for Neural Machine Translation with Learned Metrics (2021, methodology): “Most experiments were performed on NAVER Smart Machine Learning (NSML) (Kim et al., 2018).”
Phase-aware Speech Enhancement with Deep Complex U-Net (2019, methodology): “All experiments were implemented and fine-tuned with NAVER Smart Machine Learning (NSML) platform (Sung et al., 2017; Kim et al., 2018).”
What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis (2019, methodology): “All experiments are performed with NAVER Smart Machinie Learning (NSML) platform [14].”
Rethinking Spatial Dimensions of Vision Transformers (2021, unknown): “NSML [20] has been used for experiments.”