The Asia-Pacific Automated Machine Learning (AutoML) Market was valued at USD 610 Million in 2025 and is projected to reach USD 4,980 Million by 2035, growing at a CAGR of 22.8% from 2024 to 2035. The region is witnessing robust growth driven by increased adoption of AI-based solutions across industries such as finance, healthcare, and retail. Key players in the market include DataRobot, Google, Alibaba Cloud, and H2O.ai, who are expanding their presence and investing in R&D to address the region's diverse data and automation needs.
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Market Growth Drivers
Growing demand for AI-powered decision-making in business operations is a major driver for the AutoML market. Organizations in Asia-Pacific are deploying AutoML solutions to enable faster, more accurate predictive analytics and reduce manual data science workloads. For example, Alibaba Cloud introduced EasyVision, an AutoML platform, allowing businesses to automate complex machine learning tasks with minimal human intervention, resulting in greater efficiency and improved outcomes.
Rapid digital transformation across various industries, especially in banking, healthcare, and manufacturing, is accelerating AutoML adoption. Companies like DataRobot have partnered with leading financial institutions in Japan and Australia to automate credit risk assessment and fraud detection, significantly reducing operational costs and speeding up the deployment of machine learning models.
Market Trends
The integration of AutoML solutions with cloud-native platforms is a significant trend in the Asia-Pacific region. Major cloud service providers—such as Google Cloud and Microsoft Azure—are launching regional data centers and offering seamlessly integrated AutoML toolkits. This cloud-native approach enables enterprises to achieve greater scalability, data security, and compliance, as seen with Google Cloud's AutoML expansion in Singapore and India.
Industry verticalization of AutoML platforms is gaining traction. Instead of generalized models, providers are developing sector-specific solutions—such as H2O.ai's automated machine learning pipelines for healthcare diagnostics and patient data analysis in South Korea and Taiwan. This trend is anticipated to boost adoption rates, as organizations demand more tailored, industry-relevant machine learning models.
Market Opportunities
There is a significant opportunity for AutoML vendors to address the talent gap in data science across Asia-Pacific by offering no-code and low-code platforms. Companies like Microsoft are developing user-friendly AutoML tools that empower business users and analysts to build and deploy AI models without deep technical expertise.
Expansion into emerging markets such as Indonesia and Vietnam, where digital transformation is accelerating, presents substantial growth potential. Providers like Google and DataRobot are collaborating with local partners to deliver affordable, scalable AutoML solutions tailored to small and medium enterprises (SMEs) in these regions.
The line chart illustrates the steady growth in market revenue for Automated Machine Learning (AutoML) in Asia-Pacific from 2020 to 2035. The market starts at USD 180 million in 2020 and sees rapid growth, surpassing USD 610 million in 2025 and reaching approximately USD 4,980 million by 2035. The chart reflects an accelerating CAGR of 22.8%, indicative of increasing enterprise adoption, technological advancements, and strategic partnerships fueling continuous expansion. The steep incline post-2027 highlights rising demand as more industries move toward digitization and automation of analytics workflows.
Asia-Pacific AutoML Market Share by Region (%), 2025
The pie chart for 2025 illustrates that China is the largest market, constituting 39% of the Asia-Pacific AutoML market share. Japan follows with 21%, while India and Australia account for 16% and 13% respectively. Southeast Asia collectively makes up 11% of the market. China's dominance is driven by significant investments in AI, government-backed innovation programs, and rapidly growing digital infrastructure. Japan and Australia remain strong due to their advanced financial, healthcare, and manufacturing sectors, while India's market is emerging rapidly with increasing tech adoption and digital transformation initiatives.
Recent Developments
In May 2024, DataRobot announced a strategic partnership with NTT Data in Japan to deliver vertical-specific AutoML solutions for the financial and insurance sectors. This collaboration aims to simplify model deployment and increase explainability for local regulatory compliance, making AI more accessible to traditionally risk-averse industries.
In June 2024, Alibaba Cloud rolled out a major update to its EasyVision AutoML platform, introducing advanced natural language processing (NLP) and computer vision functionalities tailored to healthcare and e-commerce clients in China and Southeast Asia. The update significantly enhances multi-language support and offers pre-built industry-specific templates.
In April 2024, Google Cloud launched its improved AutoML platform in India and Southeast Asia, featuring native integrations with BigQuery and Looker for seamless data analysis and visualization. The launch includes partnerships with leading Indian retail and telecommunications companies to accelerate large-scale machine learning initiatives.
China, India, Japan, Taiwan, Vietnam, Philippines, Singapore, Australia, South Korea, Rest of APAC
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By Type (Software, Services, Cloud-based, On-premises, Open Source, Proprietary), By Application (Healthcare, BFSI, Retail, Manufacturing, Government, Telecommunication)
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