Multi-Cloud ML Systems for Enterprise Prediction
Keywords:
Cloud computing; multi-cloud architectures; predictive enterprise analytics; data management; governance; scaling model development; training and deployment; orchestration; scheduling; resource management.Abstract
Machine learning (ML) systems can provide significant benefits for enterprises. These can span various stages of the analytics process from data preparation to model development, training, and deployment. However, practical challenges can hinder deployment in enterprise settings, especially when supporting varied use cases and high-volume, high-velocity data streams. A framework is proposed that enables ML processes to be developed, trained, and deployed at scale using distributed orchestration with multiple makeup candidates. Principles for architecting such ML solutions across multi-cloud environments are also outlined, along with considerations for secure data management and governance.
Machine learning (ML) systems can provide significant benefits for enterprises. These can span various stages of the analytics process, from data preparation to model development, training, and deployment. However, practical challenges can hinder deployment in enterprise settings, especially when supporting varied use cases and high-volume, high-velocity data streams. A framework is proposed that enables ML processes to be developed, trained, and deployed at scale using distributed orchestration with multiple makeup candidates. Principles for architecting such ML solutions across multi-cloud environments are also outlined, along with considerations for secure data management and governance.
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