Alibaba Cloud publishes machine learning algorithm on GitHub

Chinese cloud vendor releases "core codes" of its Alink platform on GitHub, uploading a range of algorithm libraries that support processes for machine learning tasks, such as online product recommendation.
Written by Eileen Yu, Senior Contributing Editor

Alibaba Group's cloud unit has published "core codes" of its Alink platform on GitHub, uploading a rang of algorithm libraries that it says support batch and stream processing. These are essential to support machine learning tasks such as online product recommendations and smart customer services. 

Alibaba Cloud said developers and data analysts could tap the codes to build software functions such as statistics analysis, machine learning, real-time prediction, personalised recommendation, and detection of anomalies. 

Developed based on unified distributed computing engine Flink, Alink supports a range of open-source data storage platforms other than Alibaba's own proprietary offering, including Kafka, HDFS, and HBase. 

Alink would be a relevant option for developers seeking big data and machine-learning tools, said Alibaba Cloud's president and senior fellow of data platform, Jia Yangqing. 

Noting that the Chinese tech vendor was one of the top 10 contributors on GitHub, Jia said: "We are committed to connecting with the open source community as early as possible in our software development cycles. Sharing Alink on GitHub underlines [this] commitment."

Alibaba itself has deployed Alink to support its e-commerce marketplace Tmall, where it pushed click-through rate of product recommendations by 4% during this year's November 11 shopping festival. 

To date, the Chinese vendor has some 20,000 contributors on GitHub. Alibaba's open source initiatives include MySQL-based AliSQL, container tool Pouch, and JStorm, a Java-based version of Apache Storm. 


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