Global Automated Machine Learning Market Report By Offering (Solution, Services), By Automation Type (Data Processing, Feature Engineering, Modeling, Visualization), By End-User (BFSI, Retail and E-Commerce, Healthcare, Manufacturing, Other End Users), By Region and Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends and Forecast 2024-2033

Report Overview

The Global Automated Machine Learning Market size is expected to be worth around USD 30.9 Billion by 2033, from USD 1.2 Billion in 2023, growing at a CAGR of 38.4% during the forecast period from 2024 to 2033.

Automated Machine Learning (AutoML) is rapidly transforming how businesses leverage artificial intelligence. By automating the process of creating machine learning models, AutoML reduces the need for specialized data scientists. This technology streamlines complex tasks, enabling organizations to implement AI solutions more efficiently.

From model selection to hyperparameter tuning, AutoML simplifies the entire process, making AI more accessible to companies with limited technical expertise. As AI becomes increasingly integral to various industries, AutoML is positioned as a critical enabler of this transition.

The AutoML market is experiencing significant growth due to rising demand for AI-driven solutions across industries. Companies are looking to gain a competitive edge through data-driven decision-making, and AutoML is a key tool in this strategy.

The market is driven by the need for faster and more accurate predictions, reduced time-to-market for AI models, and the democratization of AI technology. Key players are continuously innovating, offering advanced AutoML platforms that cater to specific industry needs. The market’s growth is further supported by increased investment in artificial intelligence technologies and favorable government regulations promoting digital transformation.

Government investments in AI and machine learning are playing a crucial role in propelling the AutoML market forward. In the United States, federal spending on AI-related initiatives reached $3.3 billion in 2022, with the Department of Defense significantly expanding its AI contracts by nearly 1,200%, from $355 million in 2022 to $4.6 billion in 2023.

The overall U.S. Federal IT budget for 2025 is projected to exceed $75 billion, with a substantial portion allocated to AI and cybersecurity. This increasing investment underscores the government’s commitment to advancing AI technologies, which is expected to drive demand for AutoML solutions that can streamline AI deployment and make it more accessible.

Internationally, countries like Japan are also investing heavily in AI infrastructure. Japan, spearheaded by the Ministry of Economy, Trade, and Industry, has allocated approximately ¥114.6 billion (about $740 million) to support the AI computing industry. Collaborations with companies like Nvidia aim to unlock the economic potential of AI in the country, further fueling the growth of AutoML platforms as businesses seek to capitalize on these advancements.

The rising global investment in AI and machine learning is a clear indicator of the growing importance of these technologies in driving economic and strategic advantages. AutoML, by reducing the complexity and cost of developing AI models, is positioned to become a key enabler in this landscape. Businesses that adopt AutoML solutions can accelerate their AI initiatives, reduce dependency on specialized data science talent, and enhance their ability to compete in a technology-driven market.

The AutoML market is set for substantial growth, supported by significant government investments and the increasing need for accessible AI tools. As more organizations recognize the value of automating the machine learning process, the demand for AutoML solutions is expected to rise, making it a critical component of the broader AI ecosystem.

Key Takeaways

• Automated Machine Learning Market was valued at USD 1.2 billion in 2023, and is expected to reach USD 30.9 billion by 2033, with a CAGR of 38.4%.
• In 2023, Solution dominated the offering segment with 56.5% due to the demand for comprehensive automated ML platforms.
• In 2023, Data Processing led the automation type segment with 32.5% driven by the need for efficient data handling.
• In 2023, North America held 42.5% of the market, reflecting its leadership in AI and machine learning technologies.

Offering Analysis

Solution dominates with 56.5% due to its crucial role in enabling efficient and scalable automated machine learning deployments.

In the Automated Machine Learning Market, the Solution segment holds the largest market share at 56.5%. This segment includes software platforms and tools that automate various aspects of the machine learning workflow, such as data preprocessing, feature extraction, model selection, and hyperparameter tuning.

The dominance of Solutions is primarily due to their ability to streamline the development and deployment of machine learning models, making advanced data analytics accessible to non-experts and reducing the time and expertise required to derive valuable insights from data.

Solutions in automated machine learning are essential for businesses seeking to leverage big data without investing heavily in specialist data science teams. These platforms provide user-friendly interfaces and pre-built algorithms that can automatically analyze data, identify patterns, and build predictive models. This capability is especially valuable in industries where quick decision-making based on real-time data is crucial.

While Solutions lead the market, Services associated with automated machine learning also play a vital role. Services include consulting, integration, and support, which help organizations implement and manage automated machine learning solutions effectively.

Automation Type Analysis

Data Processing dominates with 32.5% due to its foundational role in preparing accurate and actionable datasets for machine learning models.

In the automation type segment of the Automated Machine Learning Market, Data Processing emerges as the dominant sub-segment, accounting for 32.5% of the market. This area involves automating the initial stages of the machine learning pipeline, including data collection, cleaning, and transformation.

Automating data processing reduces the chances of human error and biases in data handling, which can significantly impact the performance of machine learning development models. It enables more consistent and scalable processing of large datasets, making it possible for businesses to handle increasingly complex and voluminous data sources.

Other automation types like Feature Engineering, Modeling, and Visualization also significantly contribute to the automated machine learning ecosystem. Feature Engineering automates the identification and creation of relevant features from raw data, enhancing model accuracy.

Modeling automates the selection and training of machine learning models, while Visualization tools automatically generate visual interpretations of data and model results, facilitating easier analysis and decision-making.

The integration of these automation types enhances the overall functionality and efficiency of automated machine learning systems, supporting a broader adoption across various industries.

End-User Analysis

BFSI dominates with 26.5% due to its reliance on complex data analysis for risk assessment, fraud detection, and customer management.

In the end-user segment of the Automated Machine Learning Market, BFSI (Banking, Financial Services, and Insurance) holds the largest share at 26.5%. This sector’s significant investment in automated machine learning is driven by the need for complex data analyses to assess risk, fraud detection and prevention, and manage customer relationships effectively.

Automated machine learning tools enable BFSI institutions to rapidly analyze large volumes of transactional data to identify patterns that might indicate fraudulent activity or calculate credit risks with greater accuracy.

The use of automated machine learning in BFSI not only enhances operational efficiencies but also improves customer service by enabling more personalized and timely offerings. As regulatory pressures increase and financial markets become more complex, the ability of BFSI institutions to quickly adapt and respond to challenges through advanced analytics becomes a competitive advantage.

In Retail and E-Commerce, automated machine learning optimizes inventory management and enhances customer experience through personalized recommendations. Healthcare uses these tools for patient data analysis and predictive diagnostics, while Manufacturing applies machine learning to predict equipment failures and optimize production processes.

Key Market Segments

By Offering

• Solution
• Services

By Automation Type

• Data Processing
• Feature Engineering
• Modeling
• Visualization

By End-User

• BFSI
• Retail and E-Commerce
• Healthcare
• Manufacturing
• Other End Users

Driver

Growing Demand for AI and Efficiency Drives Market Growth

The increasing demand for artificial intelligence (AI) across various industries is a key factor driving the growth of the automated machine learning (AutoML) market. As businesses adopt AI-driven solutions to streamline operations and improve decision-making, AutoML is gaining traction for its ability to simplify the machine learning process and make AI accessible to non-experts. This lowers barriers to entry and accelerates AI adoption.

The need for efficiency and automation is another major driver. AutoML platforms automate complex tasks such as data pre-processing, model selection, and hyperparameter tuning, significantly reducing the time and effort required for machine learning projects. This allows organizations to deploy machine learning models faster, improving productivity.

The growing volume of data generated across industries is also fueling market growth. AutoML helps businesses quickly analyze large datasets and generate insights, which is critical in sectors like finance, healthcare, and retail that rely on real-time data for decision-making.

Furthermore, the rise of cloud computing is supporting the growth of the AutoML market. Cloud-based AutoML platforms offer scalable and cost-effective solutions, allowing businesses to harness machine learning capabilities without investing in expensive hardware.

Restraint

High Costs and Data Privacy Concerns Restraint Market Growth

One of the primary factors restraining the growth of the AutoML market is the high cost associated with implementing these platforms. While AutoML simplifies machine learning processes, the initial investment in software, cloud services, and skilled personnel can be expensive, particularly for small and medium-sized enterprises (SMEs).

Data privacy concerns also act as a significant restraint. AutoML platforms require access to large datasets, raising concerns about how this data is stored, processed, and protected. Strict data protection regulations, such as GDPR, make it challenging for businesses to adopt AutoML solutions without ensuring robust security measures.

The complexity of integrating AutoML into existing IT infrastructure presents another restraint. Many businesses rely on legacy systems that may not be compatible with modern AutoML tools, requiring costly upgrades and overhauls.

A lack of trust in AI-driven solutions further restricts adoption. Businesses may hesitate to fully rely on AutoML models for critical decision-making without understanding how these models function or ensuring their accuracy and reliability.

Opportunity

Increased AI Adoption and Customization Provides Opportunities

The increasing adoption of AI across industries provides significant opportunities for players in the AutoML market. As businesses integrate AI into their operations, they seek more accessible tools to develop machine learning models without requiring deep expertise.

Customization is another key opportunity. AutoML platforms that allow users to tailor machine learning models to specific business needs have a competitive advantage. Companies that can offer customized solutions for industries such as healthcare, finance, and manufacturing stand to benefit from growing demand.

The rise of personalized services presents additional opportunities. AutoML can help businesses deliver personalized customer experience management by analyzing user data and generating insights that lead to targeted marketing, product recommendations, and more.

Partnerships between AutoML providers and cloud service platforms offer growth potential. Collaborations with cloud providers enable seamless integration of machine learning tools into existing infrastructure, expanding AutoML’s reach and usage.

Challenge

Limited Talent and Integration Complexity Challenges Market Growth

One of the key challenges in the AutoML market is the shortage of skilled professionals who can effectively operate and manage these platforms. Despite AutoML’s ability to simplify machine learning processes, some level of expertise is still required to interpret results, fine-tune models, and ensure proper integration with business workflows.

Integration complexity is another challenge. Businesses often struggle to incorporate AutoML platforms into their existing IT systems and processes. Many organizations use legacy systems that are not easily compatible with modern machine learning tools, requiring significant investments in infrastructure upgrades.

Furthermore, the challenge of managing vast amounts of data in real-time is critical. AutoML platforms require large datasets for accurate model training and prediction, and businesses may face difficulties in managing data collection, storage, and processing efficiently.

The rapid pace of innovation in AI and machine learning can make it difficult for companies to keep up. Constant updates and new features in AutoML tools require businesses to continuously invest in training and upgrading their systems to remain competitive.

Growth Factors

Cloud Integration and Growing Data Volumes Are Growth Factors

The integration of AutoML with cloud managed services is a major growth factor in the AutoML market. Cloud-based AutoML platforms offer scalability, flexibility, and cost-efficiency, allowing businesses to access powerful machine learning capabilities without needing on-premise infrastructure.

The increasing volume of data generated by businesses is another key growth driver. As industries such as healthcare, retail, and finance handle vast amounts of data, AutoML provides a solution for efficiently processing and analyzing this data to generate actionable insights. The ability to quickly analyze large datasets is particularly valuable in data-intensive industries.

Additionally, the growing need for operational efficiency is contributing to the market’s growth. AutoML helps organizations automate time-consuming tasks in machine learning workflows, such as model training, testing, and tuning. This allows companies to deploy machine learning models more quickly and at a lower cost, improving overall productivity.

The rise of AI-driven decision-making in industries like manufacturing and logistics is also fueling demand for AutoML solutions. By automating the process of creating machine learning models, businesses can make faster, data-driven decisions, improving competitiveness and operational performance.

Emerging Trends

AI Democratisation and Real-Time Analytics Are Latest Trending Factors

The democratization of AI is a key trend driving the AutoML market. As businesses look to integrate AI into their operations, AutoML platforms are simplifying the machine learning process, enabling non-experts to build and deploy AI models.

Real-time analytics is another major trend. AutoML platforms are increasingly being used to process and analyze data in real time, providing businesses with immediate insights. This is especially relevant in sectors such as finance, healthcare, and retail, where timely data can influence decision-making and customer interactions.

The rise of explainable AI is also gaining traction in the market. As businesses adopt more AI-driven tools, they demand transparency in how models arrive at conclusions. AutoML platforms are integrating explainable AI features, helping users understand and trust the machine learning models they deploy.

The trend toward hybrid AI solutions, which combine AutoML with human expertise, is shaping the market. Businesses are using these platforms to automate routine tasks while leveraging human insight for more complex decision-making, creating a balanced approach to AI adoption.

Regional Analysis

North America Dominates with 42.5% Market Share

North America leads the Automated Machine Learning Market with a 42.5% share, accounting for USD 0.53 billion. This dominance is driven by the presence of major technology companies and startups, substantial investment in AI research, and a strong culture of adopting advanced technologies across industries.

The region thrives on a highly skilled tech workforce and a business environment that aggressively pursues efficiency and innovation through automation. The widespread use of advanced analytics and data-driven decision-making in sectors such as finance, healthcare, and retail fuels the adoption of automated machine learning solutions.

North America is expected to maintain its leadership in the Automated Machine Learning Market as more companies integrate AI to gain competitive advantages. Ongoing advancements in technology and supportive regulatory policies will likely promote further growth and innovation in this sector.

Regional Mentions:

• Europe: Europe’s market share in Automated Machine Learning is growing due to its strong emphasis on data privacy and the rapid adoption of AI technologies in manufacturing and automotive industries. The region benefits from robust government support for AI initiatives.
• Asia Pacific: Asia Pacific is quickly advancing in the Automated Machine Learning Market, driven by its large IT sector and significant investments in technology development, particularly in China and India. The demand for AI-driven solutions in customer service and business operations supports the market growth.
• Middle East & Africa: The Middle East and Africa are gradually adopting automated machine learning, focusing on sectors like banking and telecom to enhance operational efficiencies. Increasing technological investments in these regions are expected to boost market development.
• Latin America: Latin America is developing its presence in the Automated Machine Learning Market amid digital transformation efforts. The region sees growing use of AI solutions to tackle business challenges and improve service delivery, indicating potential for further market penetration.

Key Regions and Countries covered іn thе rероrt

North America

• US
• Canada

Europe

• Germany
• France
• The UK
• Spain
• Italy
• Rest of Europe

Asia Pacific

• China
• Japan
• South Korea
• India
• Australia
• 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

The Automated Machine Learning (AutoML) Market is dominated by three key players: Google, Microsoft, and DataRobot, Inc. These companies lead the market with their advanced AI capabilities, strategic offerings, and significant market influence.

Google plays a critical role with its AutoML tools that simplify machine learning processes for businesses. Google’s integration of AutoML into its cloud services allows companies to build custom models with ease. Its strong AI expertise and global reach make Google a leader in this market.

Microsoft is another key player with its Azure Machine Learning platform. Microsoft’s focus on making AutoML accessible to businesses of all sizes strengthens its competitive position. Its large enterprise customer base and deep integration with other Microsoft products enhance its market influence.

DataRobot, Inc. specializes in automated machine learning, providing end-to-end solutions that automate the building, deployment, and management of AI models. DataRobot’s strong focus on AutoML and its growing customer base give it a unique advantage in the market.

These companies drive the growth of the AutoML market through their innovative platforms, strong customer relationships, and focus on making machine learning more accessible and scalable for businesses worldwide.

Top Key Players in the Market

• IBM Corporation
• Microsoft Corporation
• Oracle
• Google
• AWS
• Salesforce
• SAS Institute
• Dataiku
• Alibaba Cloud
• DataRobot, Inc.
• ServiceNow
• Other Key Players

Recent Developments

Report Scope