Automated Machine Learning Market Size & Share Analysis - Growth Trends & Forecasts (2025 - 2030)

Automated Machine Learning Market Analysis

The Automated Machine Learning Market size is estimated at USD 2.59 billion in 2025, and is expected to reach USD 15.98 billion by 2030, at a CAGR of 43.9% during the forecast period (2025-2030).

The automated machine learning landscape is experiencing rapid transformation as organizations increasingly recognize the potential of AI-driven automation in their operations. The technology sector has witnessed substantial investments, exemplified by Saudi Arabia's announcement in 2023 to invest more than USD 9 billion in its technology sector, including significant allocations for cloud infrastructure and AI capabilities. This investment trend is reflected across global markets, with major cloud providers expanding their infrastructure to support growing AutoML demands. The integration of AutoML solutions has become particularly crucial as organizations seek to bridge the gap between data science expertise and the practical implementation of machine learning solutions.

The manufacturing and automotive sectors are emerging as significant adopters of automated machine learning technologies, driven by the need for predictive maintenance, quality control, and process optimization. According to industry projections, US motor vehicle production is expected to reach 11.7 million units by 2025, creating substantial opportunities for automated machine learning implementation in manufacturing processes. The technology is being leveraged to enhance production efficiency, reduce downtime, and improve quality control through automated inspection systems. Major automotive manufacturers are increasingly incorporating AutoML solutions into their digital transformation strategies, focusing on everything from supply chain optimization to predictive maintenance.

The financial services sector has emerged as a key adopter of automated machine learning solutions, particularly in risk management and fraud detection applications. In Japan alone, credit card fraud resulted in approximately 43.7 billion yen in damages in 2022, highlighting the critical need for advanced fraud detection systems. Financial institutions are increasingly turning to AutoML platforms to develop and deploy sophisticated fraud detection models, credit risk assessment systems, and customer behavior analysis tools. The technology's ability to continuously learn and adapt to new patterns has made it particularly valuable in identifying emerging fraud schemes and reducing false positives.

The retail and e-commerce sectors are witnessing significant transformation through automated machine learning adoption, particularly in customer experience optimization and inventory management. According to industry data, UK e-commerce user penetration is projected to reach 89.6% by 2025, indicating the growing importance of automated solutions in managing online retail operations. Recent developments in 2023 include Google Cloud's launch of new AI technologies specifically designed to help retailers transform their in-shelf checking processes and enhance e-commerce experiences. These innovations enable retailers to implement dynamic pricing strategies, personalized recommendations, and automated inventory management systems, significantly improving operational efficiency and customer satisfaction.

Automated Machine Learning Market Trends

Increasing Demand for Efficient Fraud Detection Solutions

The financial services industry is witnessing an unprecedented surge in the adoption of automated machine learning solutions for fraud detection, driven by the increasing sophistication of financial crimes and the need for real-time detection capabilities. According to the Japan Consumer Credit Association, the damage caused by credit card fraud reported by Japanese companies amounted to approximately 43.7 billion Japanese yen in 2022, showing a significant increase from about 33 billion yen in the previous year. This escalating threat landscape has prompted financial institutions to implement more sophisticated fraud detection mechanisms, with several players in the financial services industry now actively incorporating AI and ML technologies into their ecosystems to leverage the vast data available from newly acquired digital channels.

The banking sector's transformation toward digital channels has intensified the need for robust fraud detection solutions capable of providing real-time, actionable alerts. Financial institutions are increasingly implementing AutoML tools to address the highly skewed and imbalanced nature of fraud detection datasets, which presents unique challenges in traditional manual analysis. In September 2023, Fujitsu, along with the Hokuriku Bank Ltd and the Hokkaido Bank Ltd, announced joint trials to explore the use of generative AI for banking operations, including fraud detection and internal inquiry responses. These developments highlight how automated machine learning is becoming instrumental in helping financial institutions replace manual monitoring systems with more efficient, automated solutions that can process vast amounts of transaction data in real-time while maintaining high accuracy levels.

Growing Demand for Intelligent Business Processes

The adoption of intelligent business processes powered by AutoML is experiencing significant momentum across industries, with organizations increasingly recognizing the value of data-driven decision-making and process automation. According to recent industry data, approximately 73% of businesses in the United States plan to utilize more artificial intelligence and machine learning in cybersecurity tools, indicating the growing trust in automated intelligent processes. This trend is particularly evident in the retail sector, where major brands such as Walmart, Amazon, Target, and Alibaba are implementing machine learning technology for faster checkout, better delivery route mapping, and personalized product recommendations based on individual web browsing and purchase history.

The strategic importance of intelligent business processes is further emphasized by significant government initiatives and investments worldwide. China has demonstrated its commitment by declaring a goal to become a USD 150 billion AI global leader by 2030, while France has invested USD 1.8 billion in AI research initiatives. In January 2023, Google Cloud launched four updated and new AI technologies to help retailers transform their in-store shelf-checking processes and augment their e-commerce sites with more natural and fluid online shopping experiences. These developments showcase how machine learning growth is enabling organizations to create more efficient workflows, enhance customer experiences, and drive innovation across various business functions. The technology's ability to offer faster and more efficient delivery based on past customer data, better inventory planning, and real-time dynamic adjustment of prices has made it an essential tool for modern business operations.

Segment Analysis: By Solution

Standalone/On-Premise Segment in Automated Machine Learning Market

The Standalone/On-Premise segment continues to dominate the automated machine learning market, commanding approximately 52% market share in 2024. This significant market position is primarily driven by organizations' increasing focus on data protection and security requirements, as the solution allows data to be housed locally in user facilities, giving customers complete control over their sensitive information. The segment's prominence is particularly notable in highly regulated industries such as banking, healthcare, and government sectors, where data sovereignty and compliance requirements are paramount. Organizations are increasingly choosing on-premise deployments for their ability to be highly customized to specific processes and regulatory requirements, while also eliminating the need for monthly expenses through structured annual or multi-layer plans. The segment's strength is further reinforced by its ability to handle massive datasets that may be too large or costly to move to the public cloud, making it an ideal choice for enterprises with extensive data processing needs.

Cloud-based Segment in Automated Machine Learning Market

The Cloud-based segment is experiencing remarkable growth in the automated machine learning market, with an expected growth rate of approximately 47% during the forecast period 2024-2029. This accelerated growth is driven by organizations' increasing recognition of the cost and resource benefits of shifting their data to the cloud rather than developing and maintaining new data storage infrastructure. The segment's expansion is further fueled by its ability to offer superior flexibility in scaling computing resources up or down based on demand, making it particularly attractive for businesses with varying workload requirements. Cloud-based AutoML solutions are gaining traction among organizations looking to democratize AI capabilities across their workforce, as they provide easier access to sophisticated machine learning tools without the need for extensive infrastructure investment. The segment is witnessing increased adoption across various industries, particularly among small and medium-sized enterprises that value the pay-as-you-go model and the ability to access cutting-edge ML capabilities without significant upfront investments.

Segment Analysis: By Automation Type

Visualization Segment in Automated Machine Learning Market

The Visualization segment has emerged as the dominant force in the automated machine learning market, commanding approximately 30% market share in 2024. This segment's leadership position is driven by the increasing need for organizations to transform complex data into easily interpretable visual representations. Visualization automation is one of the primary aspects of automated machine learning, with most AutoML solutions offering a high percentage of automation for visualization capabilities. The segment's growth is fueled by the rising demand from various industries to extract insights efficiently from complex data sets, as manual data visualization has become increasingly time-consuming and challenging for organizations dealing with large datasets. Major companies like DataRobot, Compellon, Tazi.ai, and H2O.ai are offering comprehensive visualization automation capabilities in their AutoML solutions, helping organizations quickly generate meaningful visualizations from complex data without extensive manual effort.

Data Processing Segment in Automated Machine Learning Market

The Data Processing segment is positioned as the fastest-growing segment in the automated machine learning market, with an expected growth rate of approximately 46% during 2024-2029. This remarkable growth trajectory is driven by organizations increasingly seeking efficient ways to process and derive insights from large datasets. The segment's rapid expansion is supported by the exponential growth of data volumes and the rising need for automated solutions to simplify the process of building and deploying machine learning models. Data processing automation is becoming increasingly critical as organizations face the challenge of handling massive datasets efficiently while ensuring data quality and consistency. The high degree of automation in this segment is currently limited to select companies that provide advanced automation capabilities, creating significant opportunities for innovation and market expansion in the coming years.

Remaining Segments in Automated Machine Learning Market by Automation Type

The Feature Engineering and Modeling segments play crucial roles in completing the automated machine learning ecosystem. Feature Engineering focuses on leveraging data to create new variables and simplify data transformations while enhancing model accuracy, with several companies updating their capabilities to help data teams improve feature engineering workflows. The Modeling segment concentrates on training machine learning algorithms to predict labels from features, tuning them for specific business needs, and validating them on holdout data. Both segments contribute significantly to the market's overall growth by enabling organizations to automate complex machine learning processes and make AI more accessible to a broader range of users.

Segment Analysis: By End Users

BFSI Segment in Automated Machine Learning Market

The Banking, Financial Services, and Insurance (BFSI) sector continues to dominate the automated machine learning market, commanding approximately 26% market share in 2024. This significant market position is driven by the sector's increasing adoption of AI and machine learning technologies to enhance operational efficiency and improve customer experience. Financial institutions are leveraging automated machine learning solutions for various applications including fraud detection, risk assessment, customer service automation, and personalized banking services. The integration of chatbots, account handling assistance, and technical support systems has become increasingly prevalent as financial institutions seek to connect and serve customers through digital channels. Moreover, banks are enhancing their services to offer better customer experience while managing risk and meeting increasing governance and regulatory requirements. The sector's leadership is further strengthened by the growing implementation of AI-powered solutions for credit scoring, algorithmic trading, and regulatory compliance.

Healthcare Segment in Automated Machine Learning Market

The healthcare sector is experiencing remarkable growth in automated machine learning adoption, with a projected growth rate of approximately 46% during 2024-2029. This accelerated growth is primarily driven by the increasing comfort with cloud technologies and the rising urgency to utilize big data for operational and clinical improvements. Healthcare providers are increasingly implementing automated machine learning algorithms for complex diagnosis and treatment challenges, particularly in identifying and diagnosing diseases that are traditionally difficult to detect. The technology is proving especially valuable in early-stage cancer detection and genetic disease identification. Furthermore, the integration of automated machine learning in healthcare is revolutionizing patient care through improved diagnostic accuracy, personalized treatment recommendations, and enhanced operational efficiency in healthcare facilities. The sector's growth is also supported by increasing investments in AI-powered healthcare solutions and the rising demand for automated diagnostic tools and predictive healthcare analytics.

Remaining Segments in Automated Machine Learning Market by End Users

The automated machine learning market encompasses several other significant segments including Manufacturing, Retail, and E-commerce, and other end-users. The manufacturing sector is leveraging AutoML for quality control, predictive maintenance, and supply chain optimization, particularly in automotive and industrial applications. The retail and e-commerce segment is utilizing automated machine learning for customer behavior analysis, inventory management, and personalized product recommendations, enhancing the overall shopping experience. These segments are witnessing substantial adoption rates as businesses across these sectors recognize the value of automated machine learning in improving operational efficiency, reducing costs, and gaining competitive advantages. The technology's ability to handle complex data analysis, automate decision-making processes, and provide actionable insights has made it an essential tool across these diverse industry verticals.

Automated Machine Learning Market Geography Segment Analysis

Automated Machine Learning Market in North America

North America has established itself as a dominant force in the automated machine learning market, driven by its strong innovation ecosystem and strategic federal investments in advanced technology. The region benefits from the presence of visionary scientists, entrepreneurs, and recognized research institutions that have been instrumental in driving AutoML development. The United States and Canada have shown significant adoption across various sectors, particularly in healthcare, financial services, and manufacturing industries. The presence of major technology giants and a robust startup ecosystem has further accelerated market growth through continuous innovation and development of advanced AutoML solutions.

Automated Machine Learning Market in United States

The United States leads the North American market with approximately 88% share of the regional market in 2024. The country's dominance is attributed to its comprehensive approach to AI and ML adoption across various sectors. The strong commercial applications for federated ML and increasing focus on cybersecurity tools have been key growth drivers. The healthcare sector has shown particular interest in automated machine learning market solutions for drug discovery and disease prevention. The region's machine learning market is evolving rapidly due to cloud and serverless computing capabilities, allowing creators to deploy ML applications quickly and efficiently. The presence of major technology companies and their continuous investment in research and development has further strengthened the country's position in the market.

Automated Machine Learning Market in Canada

Canada emerges as the fastest-growing market in North America with a projected growth rate of approximately 46% during 2024-2029. The country's growth is driven by increasing adoption of AI technologies in government programs and services, guided by clear values, ethics, and rules. Canadian organizations are increasingly leveraging automated machine learning solutions for data processing and security applications. The educational sector's growing interest in AI and machine learning, particularly in automotive and mobility sectors, has created new opportunities for market expansion. The country's focus on developing efficient fraud detection solutions and intelligent business processes has further accelerated the adoption of AutoML technologies across various industries.

Automated Machine Learning Market in Europe

Europe has emerged as a significant market for automated machine learning solutions, with strong adoption across the United Kingdom, Germany, and France. The region's growth is driven by substantial investments in AI research and development, particularly in pharmaceutical and healthcare sectors. The presence of some of the world's largest pharmaceutical companies and a growing number of healthcare AI startups has created a robust ecosystem for AutoML adoption. The region's focus on developing digital capabilities and promoting new technologies has further accelerated market growth across various industries.

Automated Machine Learning Market in Germany

Germany maintains its position as the largest market in Europe, commanding approximately 28% of the regional market share in 2024. The country's leadership is supported by its comprehensive Digital Strategy 2025 program and strong manufacturing sector adoption. The automotive industry, being one of the region's most important sectors, has been actively implementing AutoML solutions for quality assurance and process optimization. The country's approach to building digital ecosystems emphasizes open systems, diversity, and plurality, which has created a favorable environment for automated machine learning adoption across various industries.

Automated Machine Learning Market in United Kingdom

The United Kingdom demonstrates strong growth potential with a projected growth rate of approximately 45% during 2024-2029. The country's growth is driven by its strategic positioning at the forefront of AI/ML development and strong governance frameworks. The UK government's National AI Strategy and the launch of various initiatives, including the AI Standards Hub pilot program, have created a supportive environment for automated machine learning adoption. The healthcare sector has shown particular interest in automated machine learning market solutions, with increasing implementation in patient care and operational efficiency improvements.

Automated Machine Learning Market in Asia-Pacific

The Asia-Pacific region represents a dynamic market for automated machine learning solutions, with significant developments across China, Japan, and South Korea. The region's growth is characterized by increased investment in information technology and rising adoption of FinTech solutions. Government initiatives promoting AI integration across multiple industries have created favorable conditions for market expansion. The region's focus on training and developing advanced skills by incorporating AI and ML in education has further strengthened the machine learning market foundation.

Automated Machine Learning Market in China

China leads the Asia-Pacific market as the largest country in terms of machine learning market size. The country's dominance is supported by substantial investments in robotics and other artificial intelligence applications, particularly in the military and defense industry. The adoption of smart manufacturing and governmental efforts to accelerate digital transformation have created significant opportunities for AutoML solutions. The country's regulatory environment continues to evolve to accommodate AI and data privacy concerns, which is crucial for the responsible deployment of automated machine learning.

Automated Machine Learning Market in South Korea

South Korea emerges as the fastest-growing market in the Asia-Pacific region. The country's growth is driven by significant investments in developing advanced technologies like AI and ML. Major Korean ICT companies, including leading electronics companies and internet firms, are aggressively pursuing AI-related technologies. The country's focus on implementing automated machine learning solutions across various sectors, particularly in credit evaluation and financial services, has created substantial growth opportunities.

Automated Machine Learning Market in Rest of the World

The Rest of the World region, encompassing the Middle East, Africa, and other emerging markets, shows significant potential in the automated machine learning market. The region has witnessed rapid growth in ML adoption, particularly in the automotive industry and government sectors. The United Arab Emirates and Saudi Arabia lead the region's technology adoption, with substantial investments in digital transformation initiatives. The increasing focus on cloud infrastructure development and the growing recognition of AI's importance for future growth have created favorable conditions for AutoML adoption. These regions are actively implementing effective policies and commitments to encourage the use of technology in routine tasks and business dealings.