Alibaba Cloud releases Alink machine learning algorithm to GitHub

Alibaba Cloud has announced it has made the ‘core codes’ of its machine learning algorithm Alink available on GitHub.

The company notes it is one of the top 10 contributors to the GitHub ecosystem, with approximately 20,000 contributors. Alink was built as a self-developed platform to aid batch and stream processing, with applications for machine learning tasks such as online product recommendation and intelligent customer services.

Not surprisingly, Alibaba is targeting data analysts and software developers to build their own software focusing on statistical analysis, real-time prediction, and personalised recommendation.

“As a platform that consists of various algorithms combining learning in various data processing patterns, Alink can be a valuable option for developers looking for robust big data and advanced machine learning tools,” said Jia Yangqing, Alibaba Cloud president and senior fellow of its data platform. “As one of the top 10 contributors to GitHub, we are committed to connecting with the open source community as early as possible in our software development cycles.

“Sharing Alink on GitHub underlines our such long-held commitment,” Jia added.

With the US enjoying a well-earned holiday rest, and the majority of the world hunting out Black Friday deals, Alibaba had a chance to rush the opposition with Singles Day earlier this month. The numbers put out by the company did not disappoint: zero downtime was claimed, with $1 billion of gross merchandise volume achieved within 68 seconds of launch.

A recent report from ThousandEyes aimed to explore benchmark performance of the hyperscalers, noting that Alibaba, alongside Amazon Web Services (AWS), relied more heavily on the public internet rather than Microsoft and Google, who generally prefer private backbone networks. The report also noted that, contrary to opinion, Alibaba suffered packet loss when it came to China’s Great Firewall.

You can take a look at the Alibaba Cloud Alink GitHub by visiting here.

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