Learn how to Develop and Deploy a Gradient Boosting Machine Model

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GBM is one the hottest machine learning methods. Learn how to create GBM using SciKit-Learn and Python and understand the steps required to transform features, train, and deploy a GBM.

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Register Here Is your organization ready to deploy analytic models at scale? Are your existing systems connected in the right ways to leverage the latest analytics capabilities? Join us for a live webinar detailing the creation and deployment of gradient boosting machine models using Python, Kafka and FastScore. This webinar, led by Open Data’s Matthew Mahowald, will increase your understanding of the benefits of gradient boosting as well as the easiest way to deploy and maintain a live streaming gradient boosting machine model in production systems. Our webinar will focus on providing 3 key takeaways: Learn how to create a gradient boosting machine using SciKit Learn and Python Understand the steps required to transform features, train, and deploy a GBM using FastScore, a language agnostic analytic engine See a live demo of a GBM analyzing auto insurance risk Jan 25, 2017 - 10am PST (1pm EST) Register now! Matthew is a data scientist and software engineer at Open Data Group. He holds a PhD in Mathematics from Northwestern University, where he researched the geometry of string theory and topological field theory. At ODG, Matthew focuses on developing and deploying machine learning models with FastScore.

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关键词：gradient Boosting Develop machine model understand connected learning creation existing

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