MLOps for R with Azure Machine Learning - David Smith

January 31, 2020 David Smith
Azure Machine Learning service (Azure ML) is Microsoft’s cloud-based machine learning platform that enables data scientists and their teams to carry out end-to-end machine learning workflows at scale. With Azure ML's new open-source R SDK and R capabilities, you can take advantage of the platform’s enterprise-grade features to train, tune, manage and deploy R-based machine learning models and applications. In this talk, the attendees will learn how to: •Carry out ML workflows using the authoring experience of their choice, from no-code to code-first options that include Azure ML’s drag-and-drop visual interface for defining workflows and RStudio Server on the Data Science Instance, a hosted VM workstation, for using the Azure ML R SDK from the RStudio browser-based interface. •Use the Azure ML R SDK to manage cloud resources and train, hyperparameter tune, and log and visualize metrics for their models at scale on Azure compute. •Build ML Pipelines in R for defining and orchestrating reusable and reproducible ML workflows. •Deploy, manage, and monitor their R ML models and applications as web services on Azure Container Instance and Azure Kubernetes Service, with an emphasis on robust DevOps and CI/CD for orchestrating and streamlining their end-to-end data science development lifecycle.

About the Author

David Smith

David is a Cloud Advocate for Microsoft, specializing in the topics of artificial intelligence and machine learning. Since 2009 he has been the editor of the Revolutions blog where he writes regularly about applications of data science with a focus on the programming language "R", and is also a founding member of the R Consortium. He lives with his husband and two Jack Russell terriers in the San Francisco Bay Area. Follow David on Twitter as @revodavid.

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