Cornell Lab of Ornithology Improves Machine Learning Workflow with Open Source Cloud Solution

For the last 14 years, the Cornell Lab of Ornithology has been collecting millions of bird observations through a citizen science project called eBird. This data can be used to model and understand the distribution, abundance and movements of birds across large geographic areas and over long periods of time, which yields priorities for broad-scale bird conservation initiatives. Previously, researchers at the lab used mid-sized traditional academic high performance computers, with modeling runs times of 3 weeks for a single species. By moving their open-source workflow to Microsoft’s scalable Azure HDInsight service, the researchers were able to reduce their analysis run times to 3 hours, generating results for more species and providing quicker results for conservation staff to use in planning. Situation Hosted at the Cornell Lab of Ornithology, eBird…


Link to Full Article: Cornell Lab of Ornithology Improves Machine Learning Workflow with Open Source Cloud Solution

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