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: This work introduces the approach to structure MLWfs’ components and metadata in order to aid component retrieval and reuse of new MLW FS and shows a practical use case in the Oil & Gas industry and evaluates the feasibility of the proposed technique.
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Abstract: Machine Learning Workflows (MLWfs) have become an essential and disruptive approach in problem-solving over several industries. However, the development process of MLWfs may be complex, time-consum...
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TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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Rafiki: machine learning as an analytics service system
Wei Wang,Jinyang Gao,Meihui Zhang,Sheng Wang,Gang Chen,Teck Khim Ng,Beng Chin Ooi,Jie Shao,Moaz Reyad +8 more
- 01 Oct 2018
TL;DR: Rafiki is developed and presented to provide the training and inference service of machine learning models, and facilitate complex analytics on top of cloud platforms, and provides distributed hyper-parameter tuning for the training service, and online ensemble modeling for the inference service which trades off between latency and accuracy.
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First Steps in Seismic Interpretation
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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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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