Global Automated Machine Learning Market Size, Share, Statistics Analysis Report By Offering (Solution, Services), By Enterprise Size (SMEs, Large Enterprises), By Deployment (Cloud, On-premises), By Application (Data Processing, Feature Engineering, Model Selection, Hyper-parameter, Optimization Tuning, Model Ensembling, Others), By Vertical (BFSI, Retail & E-commerce, Healthcare, Government & Defense, Manufacturing, Media & Entertainment, Automotive & transportation, IT & Telecommunications, Others), Region and Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends and Forecast 2025-2034

Report Scope

The Global Automated Machine Learning Market is expected to be worth around USD 231.54 Billion by 2034, up from USD 4.5 Billion in 2024. It is expected to grow at a CAGR of 48.30% from 2025 to 2034.

In 2024, North America held a dominant market position, capturing over a 46.4% share and earning USD 2.08 Billion in revenue. Further, the United States dominates the market by USD 1.67 Billion, steadily holding a strong position with a CAGR of 47.1%.

​Automated Machine Learning (AutoML) refers to the process of automating the end-to-end process of applying machine learning to real-world problems. AutoML aims to make machine learning more accessible by automating tasks such as data preprocessing, feature engineering, model selection, and hyperparameter tuning. This approach allows non-experts to develop machine learning models without requiring deep expertise in the field. ​

The rapid growth of the AutoML market is primarily driven by the increasing demand for intelligent automation across various industries. Businesses are actively seeking solutions that can streamline operations, reduce costs, and enhance decision-making processes. AutoML platforms address these needs by automating complex machine learning workflows, enabling organizations to deploy AI solutions more efficiently.

Key Takeaways

• Market Value Growth: The market value is expected to grow from USD 4.5 billion in 2024 to USD 231.54 billion by 2034, indicating a significant increase.
• CAGR: The market is projected to grow at a CAGR of 48.30%.
• Market by Offering: Solutions account for 68.8% of the market share.
• Market by Enterprise Size: Large enterprises represent 74.5% of the market share.
• Market by Deployment: Cloud-based deployment holds 60.2% of the market share.
• Market by Application: Data processing accounts for 34.6% of the market share.
• Market by Vertical: The BFSI sector represents 32.7% of the market share.
• Regional Breakdown: North America holds 46.4% of the global market share.
• US Market Value: The market value in the US is USD 1.67 billion.
• US CAGR: The US market is growing at a CAGR of 47.1%.

Analyst’s Review

There is a growing demand for AutoML solutions across various sectors, including healthcare, finance, retail, and manufacturing. These industries are increasingly adopting AutoML to improve operational efficiency, enhance customer experiences, and drive innovation.

For instance, healthcare providers are utilizing AutoML for predictive analytics in patient care, while financial institutions leverage these tools for fraud detection and risk assessment. This widespread adoption is fueling the demand for user-friendly and efficient AutoML platforms. ​

The AutoML market presents significant opportunities for growth, particularly in emerging markets and among small and medium-sized enterprises (SMEs). As businesses strive to harness the power of AI without extensive investments in specialized talent, AutoML offers a viable solution.

The availability of no-code and low-code AutoML platforms is democratizing access to machine learning, allowing a broader range of users to develop and deploy AI models. This trend opens up new avenues for service providers to offer tailored solutions and support to businesses embarking on their AI journeys. ​

Technological advancements are continually enhancing the capabilities of AutoML platforms. Integrating generative AI and large language models (LLMs) into AutoML systems is improving model training processes and expanding the range of tasks that can be automated.

These innovations are making AutoML tools more powerful and versatile, enabling users to tackle more complex machine learning challenges with greater ease. Additionally, the development of cloud-based AutoML services is providing scalable and cost-effective solutions for businesses, further accelerating the adoption of AutoML technologies. ​

Key Statistics

Adoption and Usage

• 48% of businesses are using machine learning, deep learning, data analysis, and natural language processing to effectively make use of large data sets.
• AutoML tools streamline the process of selecting, training, and tuning ML models, making it accessible to non-experts.

Investment and Budget Allocation

• Businesses allocate up to 20% of their tech budget to AI, including AutoML technologies.
• 58% of companies will increase AI investments in 2025, which is likely to include AutoML solutions.

Applications and Use Cases

• 25% of IT professionals want to use machine learning, including AutoML, for security purposes.
• 16% of IT professionals find machine learning effective and efficient for marketing and sales.

Productivity and Revenue Impact

• The use of AI, including AutoML technologies, has increased business productivity by about 54%.
• 80% of surveyed respondents report that AI and machine learning are boosting revenues.

Executive Involvement

• 75% of all artificial intelligence projects, which may include AutoML initiatives, are personally overseen by C-level executives.

Workforce Impact

• AI, including AutoML technologies, is estimated to create 133 million new jobs by 2030.
• 37% of business leaders plan to upskill their employees in AI-related skills, including AutoML, in the next two or three years.

Regional Analysis

United States Market Size

In North America, The United States dominates the Automated Machine Learning market with a market size of USD 1.67 billion, holding a strong position steadily with a robust CAGR of 47.1%. This significant growth is fueled by increasing adoption of AI-driven automation across various sectors, including finance, healthcare, and retail. As businesses continue to integrate automated machine learning solutions to streamline operations and enhance decision-making, the demand for AutoML platforms is surging.

The cloud-based deployment model is particularly popular in the region, offering scalable, cost-effective solutions that meet the needs of large enterprises. Cloud deployment accounts for a substantial portion of the market, reflecting a shift towards more flexible, on-demand services. Additionally, the focus on data processing applications, which play a critical role in handling vast amounts of data, further strengthens the region’s dominance in the global AutoML market.

With its growing emphasis on AI innovation and supportive technological advancements, the United States is positioned to maintain its leadership in the market for the foreseeable future. The increasing need for businesses to leverage AI and machine learning technologies will likely continue driving the adoption of AutoML solutions, solidifying the country’s prominent position in the global landscape.

North America Market Size

In 2024, North America held a dominant market position in the Automated Machine Learning (AutoML) market, capturing more than 46.4% of the global market share, which amounted to USD 2.08 billion in revenue. This leadership can be attributed to the region’s rapid technological advancements, the widespread adoption of AI-driven solutions, and the presence of key players in the AutoML space.

The United States, in particular, continues to lead in both market size and growth potential, driven by the increasing demand for intelligent automation in industries like finance, healthcare, and retail. Additionally, North America’s robust infrastructure for cloud computing and AI research further strengthens its position, enabling businesses to scale and deploy AutoML solutions effectively.

The region benefits from a high level of investment in research and development, further accelerating the growth of machine learning technologies. Large enterprises, especially in sectors such as banking, financial services, and insurance (BFSI), are quick adopters of AutoML, seeking more efficient ways to process data and automate decision-making processes. Cloud deployment models are favored for their scalability and cost-effectiveness, contributing to North America’s significant market share.

Europe follows as the second-largest market, with countries like Germany, the United Kingdom, and France investing heavily in AI research. However, it still lags behind North America in terms of total market revenue. In contrast, the APAC region is rapidly emerging as a potential growth hub for AutoML, fueled by increasing digitalization in countries like China, India, and Japan. Despite this, North America remains the leading force, benefiting from its early adoption and sustained investment in AI and machine learning technologies.

Latin America, the Middle East, and Africa (MEA) are at the nascent stage of adopting AutoML. While these regions show promise, they still face challenges related to infrastructure and limited investment in AI-driven solutions. However, as these regions begin to prioritize technological innovation, they are expected to experience gradual growth in the coming years. Nevertheless, North America’s dominant position in 2024 is likely to continue shortly, supported by its technological maturity and leadership in AI and cloud-based solutions.

By Offering

In 2024, the Solution segment held a dominant market position in the Automated Machine Learning (AutoML) market, capturing more than 68.8% of the total market share. The reason for the leading position of the Solution segment lies in the increasing demand for comprehensive, ready-to-use AI solutions that enable businesses to automate machine learning processes without requiring deep technical expertise.

Solutions such as pre-built AutoML platforms provide businesses with tools to automate data preprocessing, model selection, and hyperparameter tuning, allowing them to deploy machine learning models quickly and effectively.

The growing trend toward AI-driven automation in various sectors, such as finance, healthcare, and manufacturing, has amplified the need for easy-to-integrate solutions that simplify complex machine learning tasks. These solutions help organizations save time and resources by reducing manual intervention and accelerating AI adoption.

As businesses continue to seek out more efficient and cost-effective ways to implement machine learning at scale, the Solution segment is expected to maintain its leading position, outpacing the Services segment, which typically focuses on customization and support.

By Enterprise Size

In 2024, the Large Enterprises segment held a dominant market position in the Automated Machine Learning (AutoML) market, capturing more than 74.5% of the total market share. This dominance is driven by the substantial resources and technical expertise that large organizations possess, enabling them to invest in and integrate sophisticated AutoML solutions across various functions.

Large enterprises, particularly in industries like banking, finance, and healthcare, are adopting AutoML to enhance decision-making, streamline operations, and improve customer experiences.

These organizations typically have the financial and operational capacity to implement AI-driven technologies at scale, which gives them an edge over Small and Medium Enterprises (SMEs). Additionally, large enterprises often require highly customized AutoML solutions to address complex, industry-specific needs, driving demand for comprehensive platforms and support services.

As the adoption of AI and automation continues to grow, large enterprises will likely remain the primary drivers of the AutoML market, outpacing SMEs, which tend to face challenges related to resource limitations and technical skill gaps.

By Deployment

In 2024, the Cloud segment held a dominant market position in the Automated Machine Learning (AutoML) market, capturing more than 60.2% of the total market share. This dominance is primarily due to the scalability, flexibility, and cost-effectiveness that cloud-based solutions offer to businesses of all sizes.

Cloud deployments allow organizations to access AutoML platforms without the need for significant upfront investments in hardware or infrastructure, which is especially beneficial for businesses looking to scale operations or expand AI capabilities.

Moreover, cloud platforms provide seamless integration with existing IT infrastructure and facilitate real-time updates, making them highly adaptable to changing business needs. Cloud-based AutoML solutions also support collaboration across geographically dispersed teams, enhancing productivity and decision-making.

The reduced operational costs, coupled with the ability to scale resources on-demand, have made cloud deployment the preferred choice for many companies. In contrast, on-premises solutions, which require heavy investments in hardware and dedicated maintenance, are less attractive for businesses seeking efficiency and agility. As a result, the Cloud segment continues to lead, and its share is expected to grow further in the coming years.

By Application

In 2024, the Data Processing segment held a dominant market position in the Automated Machine Learning (AutoML) market, capturing more than 34.6% of the total market share. This leadership can be attributed to the critical role that data processing plays in the overall machine learning workflow.

Effective data processing is essential for preparing raw data for analysis, ensuring that it is clean, well-structured, and ready for model training. As the volume of data generated across industries continues to grow, businesses are increasingly turning to AutoML solutions to automate and streamline data processing tasks, saving time and resources.

Data processing involves tasks such as data cleaning, transformation, and normalization, which are foundational to building high-quality machine learning models. Given that data is the cornerstone of any AI project, businesses prioritize efficient data handling to ensure better model accuracy and performance.

As a result, the demand for automated data processing tools within AutoML platforms has surged, propelling the segment to its dominant market position. In comparison, other applications like feature engineering or model ensembling, while important, are secondary to the need for robust data preparation.

By Vertical

In 2024, the BFSI (Banking, Financial Services, and Insurance) segment held a dominant market position in the Automated Machine Learning (AutoML) market, capturing more than 32.7% of the total market share.

The BFSI sector leads the market due to the growing need for advanced data analytics and decision-making capabilities to handle vast amounts of financial data. Financial institutions are increasingly leveraging AutoML solutions to improve fraud detection, risk management, and customer service by automating complex processes and enhancing predictive analytics.

The ability of AutoML platforms to process large volumes of transactional data, identify patterns, and provide actionable insights is crucial in the BFSI sector, where precision and speed are paramount. These solutions help financial organizations optimize their operations, reduce operational costs, and mitigate risks.

Additionally, the increasing regulatory requirements in the financial industry drive the demand for automated compliance monitoring, making AutoML an attractive tool. As a result, BFSI has emerged as the leader in AutoML adoption, outpacing other sectors such as retail, healthcare, and manufacturing, which are also adopting AI but at a slower pace.

Key Market Segments

By Offering

• Solution
• Services

By Enterprise Size

• SMEs
• Large Enterprises

By Deployment

• Cloud
• On-premises

By Application

• Data Processing
• Feature Engineering
• Model Selection
• Hyperparameter Optimization Tuning
• Model Ensembling
• Others

By Vertical

• BFSI
• Retail & E-commerce
• Healthcare
• Government & Defense
• Manufacturing
• Media & Entertainment
• Automotive & transportation
• IT & Telecommunications
• Others

Driving Factor

Increasing Demand for AI-Driven Automation

The Automated Machine Learning (AutoML) market is experiencing significant growth, primarily driven by the escalating demand for AI-driven automation across various industries. Businesses are increasingly seeking solutions that can streamline operations, enhance decision-making, and reduce time-to-market for products and services. AutoML platforms address these needs by automating complex machine learning tasks, enabling organizations to leverage data more effectively without requiring extensive expertise in data science.​

In sectors such as finance, healthcare, and retail, the ability to quickly analyze large datasets and generate actionable insights is crucial. AutoML facilitates this by providing tools that simplify data preprocessing, feature engineering, and model selection processes. For instance, in the BFSI sector, the growing need for efficient fraud detection solutions has led to a surge in AutoML adoption, as these platforms can rapidly process and analyze transaction data to identify anomalies.

Restraining Factor

High Implementation Costs and Data Privacy Concerns

Despite the promising growth prospects, the AutoML market faces significant challenges, notably high implementation costs and data privacy concerns. Deploying AutoML solutions often requires substantial investments in software, cloud services, and skilled personnel, which can be prohibitive for small and medium-sized enterprises (SMEs). The initial financial outlay for integrating AutoML into existing systems may deter some organizations from adopting these technologies. ​

Additionally, AutoML platforms necessitate access to large volumes of data, raising concerns about data storage, processing, and protection. Strict data protection regulations, such as the General Data Protection Regulation (GDPR), impose stringent requirements on how businesses handle personal data. Ensuring compliance with these regulations while utilizing AutoML can be complex and resource-intensive, further hindering widespread adoption.

Growth Opportunity

Integration of AI in Emerging Markets

A significant growth opportunity for the AutoML market lies in the integration of AI technologies in emerging markets. As countries in regions like Asia-Pacific and Latin America undergo digital transformation, there is a growing demand for AI solutions to drive economic development and innovation. For instance, China and India are investing heavily in AI research and development, creating a fertile ground for AutoML adoption.

In the manufacturing sector, the adoption of AutoML can lead to improvements in quality control and process optimization, which are crucial for emerging economies aiming to enhance industrial productivity. Similarly, in the retail sector, AutoML can help businesses analyze consumer behavior and personalize offerings, driving growth in these markets. ​

Challenging Factor

Shortage of Skilled Professionals

A notable challenge hindering the growth of the AutoML market is the shortage of skilled professionals capable of effectively operating and managing these platforms. While AutoML aims to simplify machine learning processes, a certain level of expertise is still required to interpret results, fine-tune models, and ensure proper integration with business workflows. ​

The scarcity of data scientists and machine learning experts poses a significant barrier for organizations seeking to implement AutoML solutions. This talent gap can lead to increased competition for qualified professionals, driving up salaries and potentially limiting the pool of available expertise. Consequently, businesses may struggle to fully leverage the potential of AutoML, impeding market growth and innovation.

Addressing this challenge necessitates investments in training and development programs to build a workforce proficient in AI and machine learning. Collaborations between industry and academia can also play a pivotal role in bridging the skills gap, ensuring a steady supply of professionals equipped to drive the future of AutoML.​

Growth Factors

Integration of AI and Intelligent Automation

The Automated Machine Learning (AutoML) market is experiencing significant growth, propelled by the integration of Artificial Intelligence (AI) and Intelligent Automation (IA) across various industries. Intelligent Automation combines AI with robotic process automation to streamline complex business processes, leading to increased efficiency and reduced operational costs. For instance, a survey by Alchemmy revealed that 75% of businesses acknowledge the importance of AI for future development, yet only 25% consider Intelligent Automation a “game changer” in understanding current performance.

In the financial sector, institutions like JPMorgan Chase have integrated AI tools for massive data processing, enhancing security and scalability. Similarly, AWS assists firms such as Bridgewater in streamlining complex investment strategies, demonstrating AI’s pivotal role in financial operations. These examples underscore AI’s transformative impact on business processes, driving the demand for AutoML solutions.​

Emerging Trends

Rise of Generative AI and Augmented Analytics

A notable trend in the AutoML market is the rise of Generative AI, which enables machines to create content, designs, or solutions, thereby enhancing creativity and problem-solving capabilities. In the retail sector, companies like Victoria’s Secret have utilized AI for personalized marketing, resulting in significant improvements in customer engagement metrics.

Concurrently, Augmented Analytics is gaining traction, employing machine learning and natural language processing to automate data analysis. This approach democratizes data access, allowing non-experts to derive insights and make data-driven decisions. The integration of Augmented Analytics tools facilitates the transformation of raw data into actionable intelligence, fostering a data-driven culture within organizations.

Business Benefits

Enhanced Efficiency, Decision-Making, and Customer Experience

Implementing AutoML solutions offers businesses numerous benefits, including enhanced operational efficiency, improved decision-making, and superior customer experiences. For example, National Australia Bank’s adoption of generative AI has streamlined various tasks, freeing bankers to focus more on customer interactions, thereby enhancing service quality.

Moreover, AI-driven predictive analytics enable businesses to forecast market trends and consumer behavior accurately, facilitating proactive strategies and competitive advantage. In the manufacturing sector, AI applications have led to significant improvements in quality control and process optimization, contributing to cost reductions and increased profitability. ​

Additionally, the adoption of MLOps practices has streamlined the deployment and management of machine learning models, ensuring reliability and scalability in production environments. This operational efficiency translates into faster time-to-market for AI-driven solutions, enhancing overall business agility. ​

Key Regions and Countries

• North America
  • US
  • Canada
• Europe
  • Germany
  • France
  • The UK
  • Spain
  • Italy
  • Russia
  • Netherlands
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • South Korea
  • India
  • Australia
  • Singapore
  • Thailand
  • Vietnam
  • Rest of APAC
• Latin America
  • Brazil
  • Mexico
  • Rest of Latin America
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE
  • Rest of MEA

Key Players Analysis

IBM has significantly expanded its Automated Machine Learning (AutoML) capabilities through strategic acquisitions and partnerships. In January 2025, IBM announced its intent to acquire Applications Software Technology LLC (AST), a global Oracle consultancy. This acquisition aims to bolster IBM’s expertise in Oracle Cloud Applications, particularly within the public sector, enhancing its ability to assist clients in digital transformations. ​

Oracle has been actively enhancing its AI and AutoML offerings through strategic acquisitions. In December 2022, Oracle acquired Newmetrix’s assets from Smartvid.io, integrating AI-driven construction safety solutions into its Construction Intelligence Cloud. This acquisition strengthens Oracle’s position in the construction sector by offering advanced safety analytics. ​

Microsoft has significantly bolstered its AI and AutoML capabilities through strategic acquisitions. In 2022, Microsoft acquired Nuance Communications for $19.7 billion, aiming to enhance its conversational AI and speech recognition offerings, particularly in the healthcare sector. This acquisition underscores Microsoft’s commitment to expanding its AI portfolio. ​

Top Key Players in the Market

Report Scope