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.
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 http://blog.revolutionanalytics.com 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.
