Global Automated Machine Learning Market – AI Integration & Forecast 2025–2033

Automated Machine Learning Market Size and Forecast 2025-2033

Automated Machine Learning Market is expected to reach US$ 51.63 billion by 2033 from US$ 2.70 billion in 2024, with a CAGR of 38.80% from 2025 to 2033. Growing demand for AI democratization, insufficient qualified data scientists, demands for quicker model deployment, cloud computing innovation, and growing use across industries seeking scalable, efficient AI solutions are the primary driving factors of automated machine learning, or AutoML.

Automated Machine Learning Market Report by Offering (Solution, Service), Enterprise Size (SMEs, Large Enterprises), Deployment Mode (Cloud, On-Premise), Application (Data Processing, Model Ensembling, Feature Engineering, Hyperparameter Optimization Tuning, Model Selection, Others), End Use (Healthcare, Retail, IT and Telecommunication, Banking, Financial Services and Insurance, Automotive & Transportation, Advertising & Media, Manufacturing, Others), Countries and Company Analysis 2025-2033.

Automated Machine Learning Market Overview

By automatic processing of tasks such as data preprocessing, feature choice, model choice, and hyperparameter optimization, Automated Machine Learning (AutoML) facilitates the automatic creation of machine learning models. It enables users to rapidly and efficiently create practical models, including those who know nothing about data science. For minimizing the human effort and mistakes, autoML platforms employ algorithms and optimization strategies to establish optimal configurations for the model. This automation makes machine learning more affordable, scalable, and accessible, which accelerates the uptake of AI across sectors. Ultimately, AutoML makes it possible for enterprises to tap into data-driven insights without the need for much technical proficiency.

Because of several key reasons, automated machine learning, or AutoML, is growing rapidly. First, businesses are using AutoML to enable model building without lots of information because there are not enough qualified data scientists. Second, efficient tools are necessary for processing and analyzing information quickly because there is more information and it is more complex. Third, businesses desire quicker deployment of AI models in order to maintain a competitive advantage, something that AutoML enables through automation. Fourth, scalable infrastructure to enable AutoML platforms is facilitated through advancements in cloud computing. In addition, the democratization of AI facilitates mass adoption in numerous industries such as healthcare and finance, raising the demand for AutoML solutions that ease and accelerate machine learning.

Growth Drivers for the Automated Machine Learning Market

Increasing complexity and volume of data

Among the key drivers of growth of Automated Machine Learning (AutoML) is data volume and complexity. The challenge of analyzing and processing the large amounts of data that organizations receive from many sources—such as social media, Internet of Things sensors, and business transactions—rises exponentially. Size and data complexity are too high for conventional manual machine learning methods to address. By streamlining important processes such as feature engineering, data preprocessing, and model selection, autoML addresses this issue and makes analysis faster and more accurate. Companies can derive meaningful insights from complex data without much human interaction owing to this automation, which fosters creativity and productivity. Consequently, there is an increasing demand for AutoML solutions that can effectively cope with increasing data complexity and size.

Advancements in cloud computing and infrastructure

Cloud computing and infrastructure advancements are leading drivers of Automated Machine Learning's (AutoML) growth. Organizations can analyze enormous amounts of data and run complex machine learning pipelines without the need to make significant investments in costly on-premise hardware due to cloud platforms' scalable on-demand processing power. For organizations of all shapes and sizes, these advances accelerate the pace, cost-effectiveness, and accessibility of AutoML offerings. IBM enhanced its WatsonX AI and data platform in April 2024 with the launch of Meta Llama 3, the latest version of Meta's open-source large language model. With its seamless integration with its Granite series models and industry-leading products from partners such as Meta, this upgrade expanded IBM's Watsonx.ai model collection, encouraging business AI innovation. These innovations in cloud-based AI platforms allow AutoML systems to leverage strong pre-trained models, train models faster, and deploy faster, all of which make industry-wide use and growth of AutoML possible.

Growing AI democratization

One of the main drivers of the growth of Automated Machine Learning (AutoML) is the growth in AI democratization that allows a wider group of individuals, even non-experts, to develop and deploy machine learning models. Small organizations with limited data science groups can leverage AI due to autoML's capacity to automate involved processes such as model choice, training, and tuning. Large technology collaborations and innovations further propel such democratization. For example, to accelerate the development of machine learning, Google Cloud and NVIDIA announced an expansion of their partnership. The latest NVIDIA Grace Blackwell AI computing platform was selected by Google, and the NVIDIA H100-powered DGX Cloud service was made available on Google Cloud. These advances provide developers with robust infrastructure for successfully scaling and managing generative AI applications. The AutoML market keeps growing due to these efforts, which reduce barriers to entry and promote the broader adoption of AI technologies.

Challenges in the Automated Machine Learning Market

Data Privacy and Security Concerns

Concerns about data security and privacy provide major obstacles in the market for automated machine learning (AutoML). Ensuring compliance with data protection laws like GDPR and HIPAA becomes crucial since AutoML frequently entails processing massive numbers of sensitive or personal data, particularly in industries like healthcare, banking, and government. AutoML platforms that run on the cloud may make users more vulnerable to data misuse, illegal access, and security breaches. Strong encryption, safe data storage, and access control systems must be put in place by organizations. Furthermore, third-party providers must be trusted because any flaw in their systems could jeopardize data integrity and harm an organization's reputation, which would prevent broader AutoML use.

Skill Gap in Interpreting Results

One of the main issues facing the Automated Machine Learning (AutoML) sector is the lack of expertise in analyzing outcomes. Although AutoML streamlines the process of developing models, it still necessitates a fundamental understanding of statistics, data science, and domain experience to comprehend and appropriately interpret the results. Without this context, users can ignore biases, misread model performance measures, or base bad business decisions on faulty insights. This disparity restricts the efficient deployment of AutoML, particularly in crucial applications where accountability and explainability are crucial. To close this gap and guarantee responsible and knowledgeable usage of AutoML solutions, training, better user interfaces, and improved interpretability tools are needed.

United States Automated Machine Learning Market

The growing need for AI-driven automation in sectors like healthcare, banking, and retail is driving the rapid growth of the US Automated Machine Learning (AutoML) industry. Businesses without a lot of data science experience may now more easily design and implement machine learning models thanks to autoML platforms. Microsoft acquired Nuance Communications for $19.7 billion in 2022, greatly enhancing its AI and AutoML capabilities. This calculated action highlights Microsoft's dedication to growing its AI portfolio while improving its speech recognition and conversational AI technologies, especially in the healthcare industry. Advances in AI technology and growing acceptance of automated, scalable machine learning solutions are likely to propel the U.S. AutoML market's robust expansion as businesses emphasize efficiency and creativity.

Germany Automated Machine Learning Market

Due to the strong industrial foundation and rising need for AI-driven automation, Germany's Automated Machine Learning (AutoML) market is expanding significantly. AI adoption in the manufacturing industry increased from 6% in 2020 to 13.3% in 2023, and by 2030, it is expected to have a significant economic impact. AutoML platforms are becoming increasingly popular because they make it easier to create and implement machine learning models, enabling companies without a lot of data science experience to use AI. It is anticipated that this trend will continue, establishing Germany as Europe's pioneer in AI and AutoML innovation.

India Automated Machine Learning Market

India's market for automated machine learning (AutoML) is expected to increase significantly due to the country's growing digital transformation and the use of AI in industries including manufacturing, healthcare, and finance. Businesses are increasingly using AutoML systems to streamline machine learning model building, with a focus on improving data-driven decision-making and operational efficiency. Adoption of AI technologies is further supported by government programs like the National Strategy for Artificial Intelligence. With significant investments and developments anticipated in the upcoming years, this momentum places India as a major player in the global AutoML scene.

Saudi Arabia Automated Machine Learning Market

The Saudi Arabian market for Automated Machine Learning (AutoML) is expanding quickly due to the government's Vision 2030 objective and growing digital transformation projects. AutoML is being used by businesses in a variety of industries, including healthcare, banking, and oil and gas, to speed up data processing, enhance decision-making, and maximize operational effectiveness. This growth is fueled by the accessibility of cloud infrastructure and the growing use of AI. Additionally, the expanding skill pool and regional investments in smart city initiatives increase market potential. The demand for qualified personnel and worries about data protection are obstacles. All things considered, Saudi Arabia's autoML market is expected to grow rapidly, bolstering the country's push for automation and innovation.

Automated Machine Learning Market Segments:

Offering

• Solution
• Service

Enterprise Size

• SMEs
• Large Enterprises

Deployment Mode

• Cloud
• On-Premise

Application

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

End Use

• Healthcare
• Retail
• IT and Telecommunication
• Banking, Financial Services and Insurance
• Automotive & Transportation
• Advertising & Media
• Manufacturing
• Others

Country 

North America

• United States
• Canada

Europe

• France
• Germany
• Italy
• Spain
• United Kingdom
• Belgium
• Netherlands
• Turkey

Asia Pacific

• China
• Japan
• India
• Australia
• South Korea
• Thailand
• Malaysia
• Indonesia
• New Zealand

Latin America

• Brazil
• Mexico
• Argentina

Middle East & Africa

• South Africa
• United Arab Emirates
• Saudi Arabia

All companies have been covered from 5 viewpoints:

• Company Overview
• Key Persons
• Recent Development & Strategies
• SWOT Analysis
• Sales Analysis

Key Players Analysis

• DataRobot Inc.
• Amazon web services Inc.
• dotData Inc.
• IBM Corporation
• Dataiku
• SAS Institute Inc.
• Microsoft Corporation
• Google LLC (Alphabet Inc.)
• H2O.ai
• Aible Inc.

Report Details: