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Managing Machine Learning Workflow Components
Marcio Ferreira Moreno,Vítor Lourenço,Sandro Rama Fiorini,Polyana Costa,Rafael Brandão,Daniel Civitarese,Renato Cerqueira +6 more
TL;DR: In this article, the authors introduce machine learning workflow management (MLWfM) as a technique to aid the development and reuse of MLWfs and their components through three aspects: representation, execution, and creation.
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Abstract: Machine Learning Workflows (MLWfs) have become essential and a disruptive approach in problem-solving over several industries. However, the development process of MLWfs may be complicated, hard to achieve, time-consuming, and error-prone. To handle this problem, in this paper, we introduce machine learning workflow management (MLWfM) as a technique to aid the development and reuse of MLWfs and their components through three aspects: representation, execution, and creation. More precisely, we discuss our approach to structure the MLWfs' components and their metadata to aid retrieval and reuse of components in new MLWfs. Also, we consider the execution of these components within a tool. The hybrid knowledge representation, called Hyperknowledge, frames our methodology, supporting the three MLWfM's aspects. To validate our approach, we show a practical use case in the Oil & Gas industry.
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Citations
Workflow Provenance in the Lifecycle of Scientific Machine Learning
Renan Souza,Leonardo Guerreiro Azevedo,Vítor Lourenço,Elton F. de S. Soares,Raphael Melo Thiago,Rafael Brandão,Daniel Civitarese,Emilio Vital Brazil,Marcio Ferreira Moreno,Patrick Valduriez,Marta Mattoso,Renato Cerqueira,Marco A. S. Netto +12 more
TL;DR: This work uses workflow provenance techniques to build a holistic view to support the lifecycle of scientific ML, and design decisions to build this view are contributed with a W3C PROV compliant data representation and a reference system architecture.
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A method to generate context information sets from analysis results with a unified abstraction model based on an extension of data enrichment scheme
TL;DR: A method for processing datasets in a view of building knowledge parts, which uses an extensible data enrichment scheme, which represents the relations for the object.
2
A Knowledge-Based Approach for Structuring Cyclic Workflows.
Rafael Brandão,Vítor Lourenço,Marcelo de Oliveira Costa Machado,Leonardo Guerreiro Azevedo,Marcelo Cardoso,Renan Souza,Guilherme F. Lima,Renato Cerqueira,Marcio Ferreira Moreno +8 more
- 01 Jan 2020
TL;DR: The Cycle Orchestrator is showcased, a microservices infrastructure designed to structure and manage workflows related to heterogeneous data, through a knowledge-based perspective, exploring a holistic representation called Hyperknowledge that is amenable to be consumed and reasoned upon.
Cycle Orchestrator: A Knowledge-Based Approach for Structuring Cyclic ML Pipelines in the O&G Industry.
Rafael Brandão,Vítor Lourenço,Marcelo de Oliveira Costa Machado,Leonardo Guerreiro Azevedo,Marcelo Cardoso,Renan Souza,Guilherme F. Lima,Renato Cerqueira,Marcio Ferreira Moreno +8 more
- 01 Jan 2020
TL;DR: The Cycle Orchestrator is introduced, a microservices infrastructure to structure and manage workflows related to heterogeneous data from the O&G domain through a knowledge-based perspective that leverages reasoning, explainability and collaboration among stakeholders.
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Donald A. Herron
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TL;DR: In this paper, the authors describe Dix's correlation procedure in terms of the science, data, tools, and techniques now used in seismic interpretation in the oil and gas industry.
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ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies.
Gustavo Publio,Diego Esteves,Agnieszka Ławrynowicz,Panče Panov,Larisa N. Soldatova,Tommaso Soru,Joaquin Vanschoren,Hamid Zafar +7 more
TL;DR: It is argued that exposing semantics of machine learning algorithms, models, and experiments through a canonical format may pave the way to better interpretability and to realistically achieve the full interoperability of experiments regardless of platform or adopted workflow solution.
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Rafiki: Machine Learning as an Analytics Service System
TL;DR: In this paper, the authors developed and presented a system, called Rafiki, to provide the training and inference service of machine learning models, and facilitate complex analytics on top of cloud platforms.
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