Abstract-The demand for knowledge extraction has been increasing. With the growing amount of data being generated by global data sources (e.g., social media and mobile apps) and the popularization of context-specific data (e.g., the Internet of Things), companies and researchers need to connect all these data and extract valuable information. Machine learning has been gaining much attention in data mining, leveraging the birth of new solutions. This paper proposes an architecture to create a flexible and scalable machine learning as a service. An open source solution was implemented and presented. As a case study, a forecast of electricity demand was generated using real-world sensor and weather data by running different algorithms at the same time.
Rivero, Grolinger and Capretz proposed an architecture for generic Machine Learning as a Service (MLaaS) REF , together with an initial open source implementation.
MLaaS: Machine Learning as a Service
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)
Computer Science