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    Choosing the right Machine Learning Framework

    Machine learning (ML) frameworks are interfaces that allow data scientists and developers to build and deploy machine learning models faster and easier. Machine learning is...

    Product Management for AI

    Pete Skomoroch presented “Product Management for AI” at Rev. This post provides a distilled summary, video, and full transcript. Session Summary Pete...
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    Data Science vs Engineering: Tension Points

    This blog post provides highlights and a full written transcript from the panel, “Data Science Versus Engineering: Does It Really Have To Be This Way?
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    Data for Good’s Inaugural Meetup: Peter Bull of DrivenData

    Recently, Domino hosted a meetup of over 40 data scientists who came to see Peter Bull of DrivenData deliver a wonderful talk titled Crowdsourcing...
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    Scaling Machine Learning to Modern Demands

    This is a Data Science Popup session by Hristo Spassimirov Paskov, Founder & CEO of ThinkFast. Summary Machine learning has revolutionized the...
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    AI in the Enterprise: Making Corporations Smart Again

    In this Data Science Popup session, Danny Lange, VP of AI and Machine Learning at Unity Technologies, gives an inside look at practical applications...
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    Exploring the Limits of Parallelized Machine Learning

    This week, Domino’s Chief Data Scientist, Eduardo Ariño de la Rubia, presented a webinar: Machine Learning at Scale with Amazon's X1 Instance. If you...
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    Gain Shell Access To Your Domino Instances

    Domino offers a managed, scalable compute environment that provides push-button convenience to data scientists, whether they're interested in...
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    Wisdom From Machine Learning at Netflix

    At Data By The Bay in May, we saw a great talk by Netflix's Justin Basilico: Recommendations for Building Machine Learning Software. Justin describes...
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    Model-Based Machine Learning and Probabilistic Programming in RStan

    In this recorded webcast, Daniel Emaasit introduces model-based machine learning and related concepts, practices and tools such as Bayes' Theorem,...
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    An Introduction to Model-Based Machine Learning

    This guest post was written by Daniel Emaasit, a Ph.D. Student of Transportation Engineering at the University of Nevada, Las Vegas. Daniel's...
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    Don't get Hadooped

    I have seen many companies that are interested in improving their analytical capabilities, and I noticed a disconcerting pattern: in many cases,...
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