A Cloud-Based Framework for Machine Learning Workloads and Applications

García, Álvaro López; Lucas, Jesús Marco de; Antonacci, Marica; Castell, Wolfgang zu; David, M.; Hardt, Marcus; Iglesias, L. Lloret; Moltó, Germán; Płóciennik, Marcin; Tran, Viet; Alic, Andy S.; Caballer, Miguel; Plasencia, Isabel Campos; Costantini, Alessandro; Dlugolinský, Štefan; Dūma, M.; Donvito, Giacinto; Gomes, Jorge; Heredia, Ignacio; Ito, Keiichi; Козлов, В.; Nguyen, Giang; Fernández, Pablo Orviz; Šustr, Zdeněk; Wolniewicz, Paweł

doi:10.1109/access.2020.2964386

Cited by 66 publications

(30 citation statements)

References 34 publications

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“…We run the code in a cloud service and in our local cluster. We have used the services provided in DEEP Hybrid DataCloud (López García et al, 2020 ) for the cloud, namely DEEP-as-a-Service API (DEEPaaS) and Dashboard (a web interface to a cloud of hardwares) to deploy our application as a Docker container. DEEPaaS furnishes the graphical user interface on a browser from which to trigger the training.…”

Section: Methodsmentioning

confidence: 99%

Efficient Detection of Longitudinal Bacteria Fission Using Transfer Learning in Deep Neural Networks

et al. 2021

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A very common way to classify bacteria is through microscopic images. Microscopic cell counting is a widely used technique to measure microbial growth. To date, fully automated methodologies are available for accurate and fast measurements; yet for bacteria dividing longitudinally, as in the case of Candidatus Thiosymbion oneisti, its cell count mainly remains manual. The identification of this type of cell division is important because it helps to detect undergoing cellular division from those which are not dividing once the sample is fixed. Our solution automates the classification of longitudinal division by using a machine learning method called residual network. Using transfer learning, we train a binary classification model in fewer epochs compared to the model trained without it. This potentially eliminates most of the manual labor of classifying the type of bacteria cell division. The approach is useful in automatically labeling a certain bacteria division after detecting and segmenting (extracting) individual bacteria images from microscopic images of colonies.

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Section: Methodsmentioning

confidence: 99%

Efficient Detection of Longitudinal Bacteria Fission Using Transfer Learning in Deep Neural Networks

et al. 2021

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“…This battles the limitations of ''MLBase''-like [17] models, where a single framework must be used. Furthermore, the parallel processor design, enables the implementation of complex use-cases, where more than one models are used in parallel to produce results for the next processor in the pipeline, which is not possible by systems such as [20] and [21].…”

Section: B Workermentioning

confidence: 99%

“…In the context of remote Machine Learning as a Service (MLaaS) [19], the ''PredictionIO'' [20] framework, integrated a variety of ML models into a prediction service, access to which is provided using an API and a graphical user interface. In [21] a framework that aims to provide assistance throughout machine learning task lifecycle, such as training, validation and testing, was proposed with the name ''DEEP-Hybrid-DataCloud''. That framework uses a standardized API that enables the functionality of the ML models to be exposed based on known semantics.…”

Section: Introductionmentioning

confidence: 99%

ASML: Algorithm-Agnostic Architecture for Scalable Machine Learning

Diamantis

Iakovidis

2021

IEEE Access

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Machine Learning (ML) applications are growing in an unprecedented scale. The development of easy-to-use machine-learning application frameworks has enabled the development of advanced artificial intelligence (AI) applications with only a few lines of self-explanatory code. As a result, ML-based AI is becoming approachable by mainstream developers and small businesses. However, the deployment of ML algorithms for remote high throughput ML task execution, involving complex data-processing pipelines can still be challenging, especially with respect to production ML use cases. To cope with this issue, in this paper we propose a novel system architecture that enables Algorithm-agnostic, Scalable ML (ASML) task execution for high throughput applications. It aims to provide an answer to the research question of how to design and implement an abstraction framework, suitable for the deployment of end-to-end ML pipelines in a generic and standard way. The proposed ASML architecture manages horizontal scaling, task scheduling, reporting, monitoring and execution of multi-client ML tasks using modular, extensible components that abstract the execution details of the underlying algorithms. Experiments in the context of obstacle detection and recognition, as well as in the context of abnormality detection in medical image streams, demonstrate its capacity for parallel, mission critical, task execution.

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“…Stratum [34] focuses on analytics, but it is not text based nor does it focus on multilayered deployments. The DEEP-Hybrid-DataCloud [35] framework is very similar to Stratum, and it lacks the awareness for being deployed in multiple processing layers. Summarizing, currently, there is no tool that can efficiently compete in all the different aspects of the operationalization of data-based pipelines.…”

Section: Text Based Small Technological Footprintmentioning

confidence: 99%

PADL: A Modeling and Deployment Language for Advanced Analytical Services

Díaz-de-Arcaya

Miñón

Torre-Bastida

et al. 2020

Sensors

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In the smart city context, Big Data analytics plays an important role in processing the data collected through IoT devices. The analysis of the information gathered by sensors favors the generation of specific services and systems that not only improve the quality of life of the citizens, but also optimize the city resources. However, the difficulties of implementing this entire process in real scenarios are manifold, including the huge amount and heterogeneity of the devices, their geographical distribution, and the complexity of the necessary IT infrastructures. For this reason, the main contribution of this paper is the PADL description language, which has been specifically tailored to assist in the definition and operationalization phases of the machine learning life cycle. It provides annotations that serve as an abstraction layer from the underlying infrastructure and technologies, hence facilitating the work of data scientists and engineers. Due to its proficiency in the operationalization of distributed pipelines over edge, fog, and cloud layers, it is particularly useful in the complex and heterogeneous environments of smart cities. For this purpose, PADL contains functionalities for the specification of monitoring, notifications, and actuation capabilities. In addition, we provide tools that facilitate its adoption in production environments. Finally, we showcase the usefulness of the language by showing the definition of PADL-compliant analytical pipelines over two uses cases in a smart city context (flood control and waste management), demonstrating that its adoption is simple and beneficial for the definition of information and process flows in such environments.

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A Cloud-Based Framework for Machine Learning Workloads and Applications

Cited by 66 publications

References 34 publications

Efficient Detection of Longitudinal Bacteria Fission Using Transfer Learning in Deep Neural Networks

Efficient Detection of Longitudinal Bacteria Fission Using Transfer Learning in Deep Neural Networks

ASML: Algorithm-Agnostic Architecture for Scalable Machine Learning

PADL: A Modeling and Deployment Language for Advanced Analytical Services

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