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Article

Pangea: An MLOps Tool for Automatically Generating Infrastructure and Deploying Analytic Pipelines in Edge, Fog and Cloud Layers

by
Raúl Miñón
1,*,
Josu Diaz-de-Arcaya
1,
Ana I. Torre-Bastida
1 and
Philipp Hartlieb
2
1
Digital, TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Álava Albert Einstein 28, Vitoria-Gasteiz, 01510 Álava, Spain
2
Mining Engineering and Mineral Economics, Montanuniversitaet Leoben, Erzherzog-Johann-Straße 3, 8700 Leoben, Austria
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(12), 4425; https://doi.org/10.3390/s22124425
Submission received: 2 May 2022 / Revised: 31 May 2022 / Accepted: 7 June 2022 / Published: 11 June 2022
(This article belongs to the Special Issue Recent Advances in Big Data and Cloud Computing)

Abstract

Development and operations (DevOps), artificial intelligence (AI), big data and edge–fog–cloud are disruptive technologies that may produce a radical transformation of the industry. Nevertheless, there are still major challenges to efficiently applying them in order to optimise productivity. Some of them are addressed in this article, concretely, with respect to the adequate management of information technology (IT) infrastructures for automated analysis processes in critical fields such as the mining industry. In this area, this paper presents a tool called Pangea aimed at automatically generating suitable execution environments for deploying analytic pipelines. These pipelines are decomposed into various steps to execute each one in the most suitable environment (edge, fog, cloud or on-premise) minimising latency and optimising the use of both hardware and software resources. Pangea is focused in three distinct objectives: (1) generating the required infrastructure if it does not previously exist; (2) provisioning it with the necessary requirements to run the pipelines (i.e., configuring each host operative system and software, install dependencies and download the code to execute); and (3) deploying the pipelines. In order to facilitate the use of the architecture, a representational state transfer application programming interface (REST API) is defined to interact with it. Therefore, in turn, a web client is proposed. Finally, it is worth noting that in addition to the production mode, a local development environment can be generated for testing and benchmarking purposes.
Keywords: edge; cloud; analytic pipeline; MLOps; infrastructure; mine edge; cloud; analytic pipeline; MLOps; infrastructure; mine

Share and Cite

MDPI and ACS Style

Miñón, R.; Diaz-de-Arcaya, J.; Torre-Bastida, A.I.; Hartlieb, P. Pangea: An MLOps Tool for Automatically Generating Infrastructure and Deploying Analytic Pipelines in Edge, Fog and Cloud Layers. Sensors 2022, 22, 4425. https://doi.org/10.3390/s22124425

AMA Style

Miñón R, Diaz-de-Arcaya J, Torre-Bastida AI, Hartlieb P. Pangea: An MLOps Tool for Automatically Generating Infrastructure and Deploying Analytic Pipelines in Edge, Fog and Cloud Layers. Sensors. 2022; 22(12):4425. https://doi.org/10.3390/s22124425

Chicago/Turabian Style

Miñón, Raúl, Josu Diaz-de-Arcaya, Ana I. Torre-Bastida, and Philipp Hartlieb. 2022. "Pangea: An MLOps Tool for Automatically Generating Infrastructure and Deploying Analytic Pipelines in Edge, Fog and Cloud Layers" Sensors 22, no. 12: 4425. https://doi.org/10.3390/s22124425

APA Style

Miñón, R., Diaz-de-Arcaya, J., Torre-Bastida, A. I., & Hartlieb, P. (2022). Pangea: An MLOps Tool for Automatically Generating Infrastructure and Deploying Analytic Pipelines in Edge, Fog and Cloud Layers. Sensors, 22(12), 4425. https://doi.org/10.3390/s22124425

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