Alibaba Group has launched a slew of cloud products for the global market and earmarked Asia-Pacific as a priority region for growth. And while it is a comparatively young player in the international cloud scene, the Chinese internet giant is playing up its youth, regional datacentre footprint as well as global product releases–such as Anti-Bot Service–as key differentiators against the likes of Amazon Web Services (AWS) and Google.
Recognised more widely for its e-commerce prowess, Alibaba first launched its cloud business in China almost a decade ago in 2009. It was only in 2015 that Alibaba Cloud expanded its availability beyond the Chinese market, setting up its international headquarters in Singapore and pledging US$1 billion in investment. The funds were to go towards expanding its datacentre footprint as well as building up its partner ecosystem and developing new cloud offerings.
Three years later, this week, Alibaba Cloud debuted nine products outside its domestic market and launched its Asean Partner Alliance Programme, the latter of which aimed to enrol 150 technology partners and train 600 sales and technology professionals over the next 12 months.
Asked about competition with AWS and Google, considering Alibaba’s rather late entry into the cloud market, chief cloud architect of Alibaba Cloud International, Derek Wang, described the company’s youth a good trait especially in the technology industry.
And while its cloud business might be young in the international market, Alibaba already had been nurturing its cloud abilities in China since 2009, said Wang, who spoke with ZDNet on the sidelines of the company’s cloud summit in Singapore.
He added that, compared to the likes of Google and AWS, Alibaba had more data centres in the Asia-Pacific region.
In addition, Wang said it was focused on offering localised cloud products and services for customers in Southeast Asia and the wider Asia-Pacific region. This encompassed having local teams in the different regional markets, including local technical support, as well as collaboration with local research institutions.
He also pointed to its product offerings such as Anti-Bot Service and Apsara Stack, which were unique to Alibaba.
The vendor is touting Anti-Bot Service as a tool for online businesses to safeguard against scalpers who purchase discounted goods for resale at a higher price, crawlers looking for competitive information, and hackers who create zombie accounts to collect vouchers and coupons.
Apsara Stack is described as a cloud service stack that includes big data, middleware, security, and IaaS. Its core engine is the same platform that powers Alibaba Cloud and can be implemented in the customer’s data centre as a hybrid or private cloud.
Apart from its Asia-Pacific footprint and product differentiator, Wang said Alibaba was tapping its roots and experience in China as an advantage–especially for enterprises looking to do business in the Chinese market.
“Asia-Pacific is a unique market, and as a global cloud services provider with an Asian origin, we are committed to leverage our knowledge and experience to build a sustainable regional ecosystem and enrich our offerings to meet the needs of our customers in this digital age. This new suite includes products that are highly efficient, cost effective, and some of them are the first of their kind in the industry,” he said.
According to Wang, Alibaba Cloud currently had more than 1 million paying customers. Asked how many of these were international, he declined to reveal the breakdown.
As some indication, the vendor currently is the leading public cloud provider in China, where it has a 47.6 percent market share. It is ranked third worldwide, the vendor said.
Cloud, though, remained a small part of Alibaba’s business, accounting for just 5 percent of its overall revenue for its fiscal 2018. Its cloud revenue climbed 101 percent from last year, where it contributed 4 percent of its total revenue.
It currently offers more than 180 cloud products and has 8,000 partners worldwide.
Apart from Apsara Stack and Anti-Bot Service, its global product releases this week included Machine Learning Platform for AI (PAI), which comprised tools and artificial intelligence (AI) software algorithms, IoT (Internet of Things) platform, Elasticsearch, and Hybrid Backup Recovery.
Its Data Lake Analytics offering, described as a server-less high-performance data query service that is powered by Massive Parallel Processing (MPP) architecture, will be available worldwide by end of September.
Smart city initiatives must be inclusive, benefit citizens
Alibaba this week also announced a partnership with National University of Singapore (NUS) to drive the country’s smart nation initiatives and technology research. The collaboration aimed to arm science and technology talents with knowledge from Alibaba Cloud Academy as well as provide startups a platform to pitch their ideas to venture capitalists.
Business analytics students from NUS would be offered internships with Alibaba Cloud, focusing on developments in big data, cloud computing, business analytics, and AI.
Speaking at the vendor’s cloud summit, Singapore’s Minister for Foreign Affairs and Minister-in-Charge of Smart Nation Vivian Balakrishnan underscored the importance of data analytics skillsets in an era where “data is the new oil”.
“The real value today lies in the ability to derive insights…AI, robotics, big data analytics, deep learning…that’s where the real margins are,” Balakrishnan said. He noted that governments worldwide faced a common challenge of commoditising new skills in order to create jobs and reduce inequality.
For Singapore, this meant having to dabble in analytics and drive its smart nation ambition, he said, adding that it would help ensure the local economy remained vibrant and competitive and capable of generating new job opportunities.
Governments, though, also should ensure their smart city initiatives were inclusive and beneficial to both the owners and citizens, urged Min Wanli, Alibaba Cloud’s chief machine intelligence scientist.
For example, he noted, more cameras were being deployed on highways and roads, but traffic remained congested. This revealed inaction and a disconnect, Min said, stressing the need for data to be turned into actionable insights for cities to truly be “smart”.
Speaking to ZDNet at the summit, he pointed to the overemphasis on deploying new technology without establishing how exactly it would benefit citizens as a common misstep in smart city initiatives. For instance, if cameras were being used primarily to monitor illegal parking, and not used to ease traffic conditions, then citizens would not be benefitting from such deployments, he noted.
“So that mindset needs to change. Smart cities must be inclusive,” he said. “At the end of the day, it’s taxpayers’ money [that is used to fund such initiatives] so you need to serve the taxpayers. It needs to benefit both the city and citizens.”
He added that some cities already had the necessary infrastructure in place as well as the data. “Now it’s time to monetise the data and turn that into benefits for citizens,” he said.
Asked how Singapore could further drive its smart nation plans, Min pointed to traffic control and urban planning as areas that had room for improvement. For example, more proactive control of traffic signals near the entry and exit ramps along highways could help moderate incoming or outgoing traffic, especially during peak hours, he explained, adding that this was already implemented in Hangzhou, China.
Light signals, too, could be monitored and be adaptive, in real-time, to obtain a better fulfilment of “demand and supply”. For instance, it should show green for the lane that had waiting vehicles, rather than one that was empty.
Machine learning and analytics also could be applied to urban planning, so city planners could better redevelop a district by generating predicted outcomes, based on given rules and guidance. For example, they would be able to assess how the surrounding city grid would be impacted if 300 parking lots were added to a particular space.
With Singapore currently running several trials involving autonomous vehicles, ZDNet asked Min if he would be willing to ride one with confidence. Shaking his head, the machine intelligence scientist said the technology still was a far ways from reality.
He noted that there were too many “irregularities and abnormalities” on the roads today with which autonomous vehicles would have to analyse and manage. There sometimes were conflicting data signals, for instance, where one lane might be closed for construction or repairs but the arrow on the road remained visible and pointed for cars to go ahead. This meant the vehicle’s machine learning software would have to decipher the data signal that had the stronger authoritative voice and that it had to follow, overriding the other.
“It’s good for fund-raising exercises, but too early for practical use,” he quipped, adding that tests involving autonomous vehicles should start within a confined environment such as an industrial park before gradually progressing to highways, which were more confined and controlled compared to small roads.