Global Automated Machine Learning (AutoML) Market Size (2024 – 2030)

The estimated value of the Global Automated Machine Learning (AutoML) Market was USD 1360.57 Million in 2023 and is forecasted to attain USD 16.61 Billion by 2030, reflecting a compound annual growth rate (CAGR) of 42.97% within the forecast period of 2024-2030.

The expanding landscape of the automated machine learning (AutoML) market is attributed to its ability to empower organizations in creating and utilizing predictive models without an extensive reliance on data scientists. Given the critical role of machine learning (ML) in numerous business operations, developing high-performance ML applications traditionally necessitated the expertise of specialized data scientists and subject matter experts. AutoML seeks to mitigate this dependency by enabling domain experts to autonomously construct ML applications, eliminating the prerequisite for an in-depth comprehension of statistics and ML. Over time, advancements in data science and artificial intelligence have bolstered the performance of AutoML, with its adoption poised for further growth as businesses recognize its potential. The subscription-based nature of AutoML solutions offers customers the flexibility of pay-as-you-go pricing.

The segment of Automated Machine Learning (AutoML) constitutes a swiftly expanding domain within the realm of artificial intelligence (AI). AutoML facilitates the swift creation and deployment of predictive models without the need for profound expertise in data science. The burgeoning utilization of AI and machine learning across diverse industries further propels the AutoML sector. Noteworthy applications include its use in healthcare to establish prognostic models for disease identification and management, as well as in the financial sector for risk assessment and fraud detection. Retail sectors also benefit from AutoML through applications such as demand forecasting and personalized marketing strategies.

Global Automated Machine Learning (AutoML) Market Drivers:

Increased demand for AI and machine learning solutions is driving the Automated ML market

The rising demand for AI and machine learning solutions stands as a significant driver of the Automated ML market globally. Across various industries, the burgeoning importance of AI and machine learning solutions has led to a notable surge in demand. Machine learning has emerged as a potent tool for automating predictive analyses and decision-making processes, leveraging insights from the exponential growth of data. For entities lacking the internal expertise or resources to develop ML models, AutoML platforms present an appealing solution. Applications within the healthcare sector involve disease identification and treatment, while the financial sector utilizes AutoML for fraud detection and risk assessment. The expected proliferation of AI and machine learning across industries is anticipated to drive the demand for AutoML platforms.

Cloud-based AutoML platforms are fuelling the demand for Automated Machine Learning

The increasing acceptance of cloud-based AutoML systems represents another pivotal factor fuelling the global AutoML market. These platforms, being Software as a Service (SaaS) solutions, afford users access to machine learning tools and resources from any location with internet connectivity. Compared to on-premise solutions, cloud-based AutoML platforms offer lower upfront costs, scalability, and reduced maintenance requirements. Furthermore, they stimulate innovation within the AutoML market, as vendors continually introduce new features and capabilities previously inaccessible. Cloud-based AutoML platforms emerge as an attractive option for organizations lacking the resources or expertise to maintain in-house infrastructure. The rising prominence of cloud computing is expected to further propel the demand for cloud-based AutoML platforms.

Global Automated Machine Learning (AutoML) Market Challenges:

A significant challenge facing the worldwide AutoML industry pertains to the lack of interpretability and transparency in automated machine learning models. Unlike traditional machine learning models, which data scientists can scrutinize and explain, AutoML models often operate as "black boxes," rendering it arduous to comprehend the basis of their predictions or recommendations. The lack of transparency and interpretability in AutoML models might pose challenges for businesses and organizations seeking to act upon the insights generated. Ensuring the objectivity and ethical integrity of these models could prove challenging for executives and data scientists alike. Addressing the crucial challenge of enhancing the transparency and interpretability of AutoML models is imperative for the industry's advancement.

Global Automated Machine Learning (AutoML) Market Opportunities:

One of the prime opportunities for the global AutoML industry lies in its potential expansion into emerging markets such as Asia-Pacific and Latin America. These regions are swiftly undergoing digital transformations and adopting AI and ML solutions at an accelerated pace, presenting substantial growth prospects for AutoML vendors. Additionally, there is a burgeoning demand for low-code and no-code AI solutions, which can be swiftly deployed and managed by organizations with limited to no data science expertise. This trend opens avenues for AutoML vendors to diversify their product portfolios and capture new market shares.

COVID-19 Impact on the Global Automated Machine Learning (AutoML) Market:

The impact of COVID-19 on the global AutoML industry has been multifaceted. The pandemic has, in some aspects, exerted positive effects such as accelerating digital transformations and amplifying the demand for AI and machine learning solutions across diverse sectors. Businesses sought to automate forecasting and decision-making processes, leading to increased adoption of AutoML systems. Conversely, the pandemic has negatively affected supply chains, prompting businesses to curtail expenditures, including IT budgets, thereby slowing the adoption of new technologies. Furthermore, the pandemic underscored the necessity for ethical and transparent AI solutions, thereby impeding the adoption of AutoML platforms lacking interpretability and transparency.

Global Automated Machine Learning (AutoML) Market Recent Industry Developments:

• In February 2022, Amazon Web Services (AWS) concluded the establishment of 16 AWS Local Zones in the U.S. and is slated to launch an additional 32 AWS Local Zones across 26 countries worldwide. AWS Local Zones represent a form of infrastructure deployment placing computing, storage, and other AWS services in proximity to population centers. This enables customers to deploy applications with single-digit millisecond latency closer to end-users or on-premise data centers.
• In December 2021, AWS announced six new Amazon SageMaker features designed to enhance the usability and affordability of machine learning. These features include a no-code environment for prediction creation, more precise data labeling, universal Studio notebook support, a training compiler, automatic instance selection, and serverless computing for inference. Such attributes are poised to enhance the accessibility and affordability of machine learning.

Global Automated Machine Learning (AutoML) Market Segmentation: By Solution

• Standalone
• On-Premises

The global Automated Machine Learning (AutoML) market is segmented into two main categories: standalone and on-premises solutions. Standalone solutions are predominantly favored by small and medium-sized enterprises due to their cloud-based nature, offering advantages such as lower initial costs, easy scalability, and reduced maintenance requirements. Conversely, on-premises solutions necessitate internal IT teams for maintenance and support as they are installed directly on-site. While standalone AutoML solutions are expected to lead the market due to their convenience, larger enterprises with advanced IT capabilities and regulatory or security needs tend to prefer on-premises solutions. Nevertheless, the recognition and market share of cloud-based solutions are projected to continue growing. On-premises solutions are also expected to maintain a significant market share, particularly among larger enterprises with complex IT infrastructures.

Global Automated Machine Learning (AutoML) Market Segmentation: By Automation Type

• Feature Engineering
• Data Processing
• Data Modelling
• Visualization
• Others

The automated machine learning (AutoML) market can be categorized by automation type into feature engineering, data processing, data modeling, visualization, and other segments. Data processing is poised to dominate the market due to its crucial role in machine learning, offering potential reductions in wait times and increased productivity. Feature engineering and data modeling, essential for creating accurate and efficient machine learning models, are also expected to hold substantial market shares. Although the visualization segment may have a smaller share, it remains a vital aspect of AutoML, simplifying the understanding and communication of model insights. Overall, the market share of each segment is subject to change based on the unique requirements and priorities of individual businesses.

Global Automated Machine Learning (AutoML) Market Segmentation: By End-User

• BFSI
• Retail and E-Commerce
• Healthcare
• Manufacturing
• Others

The BFSI sector has shown increased adoption of AI and machine learning to enhance operational efficiency and improve customer experiences. With a growing focus on data, there is a rising demand for machine learning applications in the BFSI sector. Automated machine learning utilizes vast amounts of data, cost-effective processing power, and efficient storage to deliver accurate and swift results. Financial institutions collaborate with fintech services to adapt to modern requirements and regulations while improving security measures. Machine learning-powered solutions enable finance companies to automate repetitive tasks through intelligent process automation, boosting overall output. The BFSI sector leads the AutoML market due to its adoption of AI and machine learning for fraud detection, risk management, and customer service. Following BFSI, the retail and e-commerce sector is leveraging AutoML for personalized marketing, supply chain optimization, and enhanced customer service. In healthcare, AutoML finds applications in drug discovery, patient diagnosis, and treatment planning. Industries such as manufacturing utilize AI and machine learning for supply chain optimization, predictive maintenance, and quality control. Other sectors like government, transportation, and education are expected to have slower adoption rates for AutoML solutions. Market share across these segments varies depending on their specific requirements and priorities. Machine learning algorithms also significantly enhance network security by identifying potential threats such as money laundering techniques.

Global Automated Machine Learning (AutoML) Market Segmentation: By Region

• North America
• Europe
• Asia Pacific
• Middle East
• Latin America

The Asia Pacific (APAC) region is anticipated to witness the fastest-growing market for AutoML, driven by increasing IT spending and adoption of FinTech solutions. APAC governments are actively integrating AI across various sectors, fostering the growth of local markets. Notably, China sees significant adoption of machine learning in financial fraud detection, product recommendations, and industrial process optimization. However, success in machine learning initiatives hinges on reliable infrastructure and clean data. Japan's AI industry is poised to expand due to the global demand for AI in robotics, speech recognition, and visual recognition. South Korea's substantial investments in advanced technologies like AI and ML are propelling industry growth. The global AutoML market is divided into North America, Europe, Asia Pacific, the Middle East, and Latin America. While North America is expected to dominate the market, Europe is also projected to hold a considerable market share due to investments in AI and machine learning technologies, along with the increasing adoption of cloud-based solutions. Market shares across regions are likely to vary based on economic, political, and technological factors, with some regions prioritizing investments in AI and machine learning for regulatory and security reasons.

Global Automated Machine Learning (AutoML) Market Key Players:

1. Datarobot Inc.
2. Amazon Web Services Inc.
3. dotData Inc.
4. IBM Corporation
5. Dataiku
6. SAS Institute Inc.
7. Microsoft Corporation
8. Google LLC
9. H2O.ai
10. Aible Inc.