增强学习在微软云计算的商业模型应用

2017-11-11 09:00-10:00
嘉宾:

李可

微软高级数据科学家
¥30.00
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视频介绍
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嘉宾介绍

李可

微软高级数据科学家
华盛顿大学统计系博士。微软公司,云计算与人工智能研究室,高级数据科学家。前亚马逊风控研究科学家,华盛顿大学客座讲师。

主题介绍

The model aims to provide the recommendation for next best action for sales actions on Azure customers. Built with reinforcement learning framework, the model identifies the optimal policy by informin' ;&,"vIE' ;&,"ct whom (customers) and what actions to take. The goal of this model is to accelerate the Azure adoption and consumption, through more efficient actions on selected customers in a timely fashion. For instance, recommending a new Azure service to the customers when the likelihood of adoption is high, or recommending a churn prevention call when the customer is at a high risk of churn. The model also searches for the more probable paths that nurture new Azure customers to the most deeply engaged customers, i.e., the path of adding a new service to the existing service combo.

 

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