Assessing China’s machine learning platforms

In a new market research report, technology analyst firm IDC offers insights on key suppliers of machine learning platforms in China

Machine learning is gathering pace in China, with enterprises employing the technology to detect financial fraud, recommend products to consumers and streamline industrial operations, among various use cases.

In a new report assessing key suppliers of machine learning platforms in China, IDC details its observations of the market and how major suppliers fare in supporting the needs of enterprises across industries.

According to the analyst company, China’s machine learning market – including hardware, software and services – was worth RMB1bn (US$139m) in 2018. But when applied in artificial intelligence (AI) applications, machine learning contributed RMB10bn to China’s AI market last year.

Chinese suppliers of machine learning platforms currently provide more than 30 classic machine learning algorithms, with support for mainstream frameworks such as TensorFlow, PyTorch and Caffe.

They are also deeply optimising the underlying hardware for machine learning workloads to provide users with efficient and flexible offerings that integrate software and hardware.

IDC grouped China’s machine learning players into three broad categories: cloud platform suppliers that help users build machine learning models quickly; startups that deliver innovations such as federated learning and automated machine learning; and big data platform companies.

The research firm said the cloud platform players have developed machine learning expertise, backed by a large user base that can be quickly converted to users of machine learning products. Startups, on the other hand, have an edge in developing leading technologies, it said.

However, the big data platform suppliers will need to catch up with the leaders in terms of technology and market share, despite touting machine learning capabilities to help users implement predictive analytics on their platforms.

On talent issues, IDC said algorithm engineers are still in short supply in China – although automated machine learning can help to plug the skills gap by enabling business users to perform modelling after some training.

In its report, IDC China analysed 13 mainstream suppliers – Alibaba Cloud, Amazon Web Services (AWS), Baidu, DataCanvas, IBM, iQubic, Kingsoft Cloud, MeritData, Neusoft, New H3C, Tencent Cloud, Transwarp and 4Paradigm – and identified the strengths of leading and major players.

Cloud platforms: Baidu

Chinese search giant Baidu has a solid foundation in machine learning. Its open source deep learning framework PaddlePaddle, the first of its kind in China, has seen high growth in the number of downloads and active users in recent years.

Baidu was also among the first homegrown suppliers to launch automated machine learning products, with its no-code EasyDL tool achieving a high level of market awareness.

In terms of commercialisation, Baidu has also been able to roll out machine learning capabilities quickly to its large customer base to extend its market reach.

Startups: 4Paradigm

4Paradigm is focused on developing AI algorithms, with a view to thorough optimisation and integration of software and hardware.

With mature enterprise-grade products and strong commercialisation capabilities, it has stood out from the pack and achieved rapid business expansion.

Big data platforms: MeritData and DataCanvas

IDC singled out Xi’an-based MeritData as a leader in machine learning platform with capabilities honed through decades of experience in serving enterprises.

Its Tempo AI machine learning product has gained traction in multiple industries, including manufacturing and energy. The company’s leading position is backed by its mature and stable products and success stories from a wide range of customers.

DataCanvas, which IDC considers a major player, mainly serves the financial sector with diversified applications such as anti-fraud and user profiling. It has also created many innovative use cases for machine learning together with its customers in sectors such as government and manufacturing.

Global cloud giant: AWS

AWS is a towering presence in the global machine learning market. According to Amazon’s internal research data, about 80% of TensorFlow projects are deployed on AWS.

In China, AWS has gained significant recognition through its automated machine learning platform SageMaker, and customers using AWS services can quickly deploy machine learning products.

AWS’s Inferentia machine learning inference chip has also been used in the development of the Open Neural Network Exchange project spearheaded by Facebook and Microsoft in late 2017.

Even as AWS dedicates substantial resources to R&D, it has consistently been able to provide users with flexible machine learning options in an open manner, IDC said.

Advice for tech buyers

Before jumping on the machine learning bandwagon, consider whether your company can develop machine learning models.

If not, choose a platform with a higher degree of automation. If your company has enough development capabilities, IDC’s advice is to try to build a machine learning platform using open source technology.

When it comes to product selection, IDC advised enterprises to go for flexible products that are easy to start with. When assessing machine learning models, also consider whether those models meet the requirements of internal applications.

Many machine learning projects fail because of inaccurate predictions made by machine learning algorithms that are not underpinned by clean data and a strong data foundation.

In the long run, said IDC, companies will need to consider products and services that can strengthen their internal data capabilities, as well as to develop core intelligent data platforms compatible with AI models.

Finally, companies should avoid limiting themselves to hardware and computing platforms to the detriment of software and applications when adopting machine learning and AI.

“It is important to choose the most suitable underlying architecture for short-term AI workloads and plan for medium- and long-term AI workloads,” said IDC.

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