Success stories with AWS Cloud

FEATURED



Company Name: VIP E Learning Technology CO., Limited


Service Description - vipkid.com.cn

Using AWS with CN Care Cyber Cloud Ltd, VIP E Learning Technology has created a stable and reliable online learning platform, which provides tens of millions of users with personalized English language learning services. VIP E Learning Technology is a leading AI-driven education technology company dedicated to providing each and every user with personalized and adaptive online learning courses.



AWS deployment has benefited VIP E Learning Technology in two aspects: First, it has led to a remarkable improvement in overall system stability, with the availability of applications reaching 99.9%. VIP E Learning Technology’s business development exhibits two features: Rapid growth in the number of users: it took only four years for its users to increase from one million to 70 million; Uneven distribution of user traffic over 24 hours each day: the peak-hour surge in traffic at noon and after six p.m. is 10 times higher than during off-peak hours.


AWS automatic O&M function allows VIP E Learning Technology’s entire system to cope with changes in traffic with ease, so as to provide users with stable and reliable access services and better user experience. Secondly, it saves both resources and manpower. In terms of resource allocation, AWS-provided functions such as AWS Auto Scaling and Amazon EC2 Spot instance have substantially reduced resource occupation cost. After Amazon EMR’s deployment, in particular, data processing speed and volume have substantially increased while low-cost instances for data processing at night effectively reduced cost. In terms of labor cost, AWS automatic management function has reduced the demand for system O&M, saving the cost of full-time O&M personnel.


Striving to improve itself as an AI-driven education technology company, VIP E Learning Technology plans to adopt more AWS products in the future, including Amazon Machine Learning and GPU instance cluster to increase data analysis speed and accelerate model training iteration that deliver better personalized learning experience to its users.


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