Automated Machine Learning Market (2026 - 2033)

Automated Machine Learning Market Summary

The global automated machine learning market size was valued at USD 4.7 billion in 2025 and is projected to grow from USD 6.2 billion in 2026 to USD 61.2 billion by 2033, at a CAGR of 38.6% from 2026 to 2033. North America dominated the global market with the largest revenue share of 28.6% in 2025. The region's leadership is driven by the strong presence of leading technology providers, cloud computing companies, and machine learning platform vendors, along with the widespread adoption of data-driven decision-making across industries.

Organizations across the U.S. & Canada are increasingly deploying automated machine learning solutions to accelerate model development, reduce dependency on specialized data science expertise, and improve operational efficiency.

This growth is attributed to Automated machine learning (AutoML’s) capability to identify discrepancies, errors, and other issues within the data, and present the user with choices, suggestions, as well as suggest outliers.  Once the expert is presented with all this information, they can seamlessly curate multiple models, saving them time and effort. Currently, AutoML open-source and commercial tools such as TPOT, H2O.ai, Google AutoML, and DataRobot are some of the best suited for streamlining the development of tasks wherein the goal is to predict an outcome/ result. These popular solutions tend to automate some or all the ML pipelines. For instance, DataRobot, the enterprise AI platform, makes data science accessible to everyone and automates the entire process of creating, deploying, and managing AI solutions at scale. It eliminates the reliance on manual workflows, automates repetitive and time-intensive steps, enables new users to build highly accurate models, and provides a fast-path for getting AI into production.

Automated machine learning is an essential process of automating iterative and time-consuming tasks. It enables developers, analysts, and data scientists to build ML models with productivity, efficiency, and high scale. AutoML has gained traction to minimize the knowledge-based resources needed to implement and train machine learning models. Moreover, Bullish demand for AutoML is mainly attributed to its ability to help enterprises boost insights and enhance model accuracy by minimizing chances for error or bias. End-users, including BFSI, healthcare, IT & telecom, and retail, are expected to inject funds into AutoML to rev up their AI efforts to create a valuable pipeline to automate data preprocessing, model selection, and pre-trained models.

Innovation in automated machine learning has led to significant advancements in various industries, transforming the way businesses operate and interact with their customers. Automation of complex processes enables organizations to speedily analyze network behavior and automatically execute required steps, enhancing processing speeds and performance. In addition, predictive maintenance using machine learning helps companies identify potential risks and predict failures, thereby increasing productivity and saving costs. Real-time business decision making is also facilitated through machine learning, allowing businesses to extract valuable insights from large datasets and make informed decisions. TinyML, a type of machine learning that runs on smaller devices, is ideal for battery-operated devices and IoT applications, reducing power consumption, latency, and bandwidth while maintaining user privacy and efficiency.

Market Dynamics

The Automated Machine Learning (AutoML) Market is experiencing significant growth, driven by the increasing demand for democratized artificial intelligence, growing adoption of data-driven decision-making, and the need to accelerate machine learning model development across industries. AutoML solutions enable organizations to automate complex tasks such as data preparation, feature engineering, model selection, hyperparameter tuning, and deployment, reducing reliance on highly specialized data science expertise. The rapid expansion of big data, cloud computing, and enterprise AI initiatives is generating vast volumes of structured and unstructured data, further accelerating demand for AutoML platforms that can deliver faster, scalable, and cost-effective machine learning outcomes.

Market Concentration & Characteristics

The Automated Machine Learning Market is moderately concentrated, characterized by the presence of leading cloud service providers, AI platform developers, and specialized AutoML solution vendors competing on automation capabilities, model accuracy, scalability, ease of deployment, and integration with enterprise data ecosystems. Key market participants include Amazon Web Services, Inc., Google LLC, Microsoft, DataRobot, Inc., H2O.ai, Databricks, Oracle, Alibaba Cloud, Akkio Inc., and Clarifai, Inc.

These companies are increasingly investing in automated model development, no-code and low-code AI platforms, cloud-native machine learning services, MLOps capabilities, and generative AI integration to strengthen their competitive positioning. Strategic partnerships, platform enhancements, product innovation, acquisitions, and continuous advancements in automated feature engineering, model optimization, and AI governance capabilities are further intensifying competition across the market.

Analyst Perspective

The Automated Machine Learning (AutoML) Market is emerging as a key component of enterprise AI strategies, driven by the growing demand for rapid AI deployment, increasing data volumes, and the need to democratize machine learning across organizations. AutoML technologies automate tasks such as data preparation, feature engineering, algorithm selection, hyperparameter tuning, and model deployment, enabling faster and more efficient machine learning adoption. Advancements in artificial intelligence, cloud computing, no-code/low-code platforms, and MLOps frameworks are further accelerating market growth. Growing use of AutoML for predictive analytics, fraud detection, demand forecasting, predictive maintenance, and operational optimization is helping organizations improve decision-making and reduce development timelines. Increasing investments in cloud-based AI platforms, generative AI technologies, and digital transformation initiatives are expected to support long-term market expansion.

Offering Insights

Based on offering, the services segment led the market with the largest revenue share of 50.9% in 2025. Automated Machine Learning services aim to simplify and automate various stages of the machine learning workflow, making it more accessible to users without extensive expertise in data science and machine learning. These services automate the process of building machine learning models, including various tasks such as, data preprocessing, feature engineering, algorithm selection, and hyperparameter tuning, allowing users to focus on the problem they want to solve rather than the intricacies of model development. Major cloud providers such as, Google Cloud, Amazon Web Services, and Microsoft Azure offer fully managed AutoML services on their cloud platforms, providing a user-friendly interface and handling the underlying infrastructure. There are also open-source AutoML libraries such as, Auto-Sklearn, AutoKeras, and Auto-PyTorch that automate machine learning tasks for specific frameworks. Some AutoML services aim to automate the entire machine learning lifecycle, from data ingestion and preparation to model deployment and monitoring, significantly reducing the time and effort required. Additionally, there are vertical-specific solutions tailored to industries or use cases such as, healthcare, manufacturing, or computer vision, leveraging domain knowledge and pre-trained models.

The solution segment is expected to register the fastest CAGR over the forecast period. AutoML solutions are designed to automate the tasks involved in developing and deploying machine learning models. This makes it easier for organizations to leverage the power of machine learning without requiring significant expertise in data science or machine learning. AutoML solutions are becoming an increasingly important tool for organizations looking to leverage the power of machine learning to gain insights from their data and make better decisions. By automating many tedious and time-consuming tasks involved in model development and deployment, AutoML platforms can help organizations accelerate their digital transformation and unlock new opportunities for growth and innovation.

Enterprise Size Insights

Based on enterprise size, the large enterprises segment led the market with the largest revenue share of 69.7% in 2025. Large businesses are increasingly adopting cloud-based automated machine learning platforms and services. The scalable and cost-effective infrastructure of cloud platforms facilitates the training and deployment of machine learning models. Services such as, Amazon Web Services (AWS), Google Cloud AI Platform, and Microsoft Azure Machine Learning provide pre-built models, distributed training capabilities, and infrastructure management, enabling large enterprises to utilize automated machine learning without substantial infrastructure investments.

The SMEs segment is expected to register a significant CAGR over the forecast period. The adoption of machine learning is rapidly growing among small and medium-sized enterprises (SMEs). With often limited resources, SMEs may need extra expertise to analyze large data sets. Machine learning platforms and technologies can automate data analysis processes, allowing SMEs to gain valuable insights from their data with minimal manual effort. This automated data analysis helps SMEs better understand customer behavior, improve inventory management, optimize marketing strategies, and make data-driven decisions.

Deployment Insights

Based on deployment, the cloud segment led the market with the largest revenue share of 53.2% in 2025. Cloud-based AutoML solutions have gained significant traction in recent years, offering businesses and organizations a convenient and scalable way to leverage automated machine learning capabilities. These solutions, such as Google Cloud AutoML, Amazon SageMaker Autopilot, and Azure AutoML, provide user-friendly interfaces and abstraction layers that simplify the process of building and deploying machine learning models, enabling users with limited machine learning expertise to leverage advanced AutoML capabilities. Moreover, cloud platforms offer virtually unlimited computing resources that can be dynamically scaled up or down based on demand, ensuring optimal performance and cost-effectiveness for AutoML workloads.

The on-premises segment is expected to register a significant CAGR over the forecast period. On-premises based AutoML solutions offer organizations the ability to leverage automated machine learning capabilities within their own infrastructure and data centers. One of the primary advantages of on-premises AutoML is the ability to keep sensitive data within the organization’s controlled environment, which is particularly important for industries dealing with sensitive information, such as healthcare, finance, and government, where data privacy and compliance regulations are stringent. In addition, on-premises AutoML solutions provide organizations with greater control and customization options, allowing them to tailor the platform to their specific needs, integrate it with existing systems and workflows, and ensure compatibility with their infrastructure and security protocols.

Application Insights

Based on application, the data processing segment led the market with the largest revenue share of 29.9% in 2025. Automated machine learning can be utilized to automate various aspects of data processing, such as data cleaning, normalization, and transformation. The automated machine learning marketstreamline the process of identifying and correcting data errors, including detecting missing values, fixing data formatting issues, and removing outliers that could impact the accuracy of machine learning models. AutoML employs techniques such as, standardization and normalization automatically. It can also transform data into more suitable formats, minimizing the risk of errors and inconsistencies. In addition, AutoML can integrate data from multiple sources, a typically time-consuming and complex task, through techniques such as, data merging and joining. By automating these tasks, AutoML significantly reduces the time and effort needed for manual data processing, enhancing the quality and accuracy of the resulting data.

Feature engineering segment is expected to register the fastest CAGR over the forecasted period. Feature engineering is a crucial step in automated machine learning (AutoML) pipelines, as it significantly impacts the performance of the resulting models. AutoML tools and libraries such as, FeatureTools, Dask-ML, and TSFRESH can automatically generate new features from raw data by applying various techniques such as, feature synthesis, feature extraction, and feature construction, reducing the manual effort required for feature engineering. For datasets consisting of data from multiple related tables or sources, AutoML tools can automatically join and combine data from these sources to generate meaningful features that capture relationships across different entities.

Vertical Insights

Based on vertical, the BFSI segment led the market with the largest revenue share of 22.2% in 2025. In recent years, artificial intelligence (AI) and machine learning technologies have been increasingly adopted in the banking, financial services, and insurance (BFSI) industry to boost operational efficiency and enhance consumer experience. As data becomes more prominent, the demand for machine learning applications in the BFSI sector continues to grow. Automated machine learning can deliver accurate and swift results using vast amounts of data, affordable processing power, and cost-effective storage. Additionally, machine learning (ML)-powered solutions enable financial firms to enhance productivity by automating repetitive tasks through intelligent process automation.

IT & telecommunications segment is expected to register a significant CAGR over the forecast period due to its high demand for intelligent automation, network optimization, and customer analytics. Increasing data complexity from IoT devices, 5G infrastructure, and cloud platforms requires scalable AI models that AutoML can rapidly generate and deploy. Telecom operators are leveraging AutoML for predictive maintenance, churn prediction, and service personalization to enhance operational efficiency and user experience. Additionally, the integration of AutoML into DevOps and data engineering workflows accelerates AI adoption, reduces model development time, and minimizes the need for specialized data science expertise across IT ecosystems.

Regional Insights

North America dominated the Automated Machine Learning Market with the largest revenue share of 28.6% in 2025. This region has been a major contributor to the development and growth of the Automated Machine Learning market. The U.S. is one of the most developed countries in the region. AutoML is a rapidly growing market in the U.S., with several key players offering solutions that range from fully automated platforms to ones that assist data scientists in building machine learning models. The market is being driven by the need for faster and more efficient ways to build and deploy machine learning models, as well as the increasing demand for artificial intelligence solutions in various industries. In recent years, there has been a significant increase in the adoption of AutoML solutions in the U.S., especially in industries such as healthcare, finance, and retail. Healthcare providers are using AutoML to analyze medical images and identify patterns in patient data, while financial institutions are using it to detect fraudulent transactions and assess credit risk. Retailers are using AutoML to personalize recommendations and improve customer engagement.

U.S. Automated Machine Learning Market Trends

The Automated Machine Learning Market in the U.S. held the largest share in the North America region in 2025. The U.S. is at the forefront of automated machine learning research and development, with major tech companies, academic institutions, and businesses actively investing in and adopting AutoML solutions. Major U.S. tech giants such as, Microsoft, Google, and Amazon are investing heavily in developing AutoML solutions and offering cloud-based AutoML services. Microsoft Azure offers Azure AutoML, which automates the end-to-end machine learning process, including data preprocessing, model selection, hyperparameter tuning, and model deployment, making AutoML accessible to users without extensive machine learning expertise. Google Cloud provides the AutoML suite of tools, which includes pre-trained models for various tasks such as, image recognition, text classification, and structured data analysis, aiming to democratize machine learning by simplifying the model development process. Amazon Web Services (AWS) offers Amazon SageMaker Autopilot, an AutoML solution that automates data preprocessing, model tuning, and deployment, allowing businesses to quickly build and deploy machine learning models without extensive coding.

Europe Automated Machine Learning Market Trends

The automated machine learning market in Europe is expected to grow significantly over the forecast period. Various academic institutions, research organizations, and companies are actively contributing to its development and adoption. Moreover, Europe has several leading academic institutions conducting research on AutoML techniques and methodologies. For instance, the University of Leiden in the Netherlands offers courses on AutoML, covering topics such as, hyperparameter optimization, meta-learning, and transfer learning. Additionally, the University of Freiburg in Germany has a dedicated research group focused on AutoML and meta-learning. These academic efforts contribute to advancing the theoretical foundations and practical applications of AutoML.

Asia Pacific Automated Machine Learning Market Trends

The automated machine learning industry in the Asia Pacific region is anticipated to be at the fastest CAGR over the forecast period. Various government initiatives are propelling the demand for Automated Machine Learnings in Asia Pacific. Fiber optic networks are playing a crucial role in supporting smart city solutions, Internet of Things (IoT) devices, and other digital innovations as part of various government initiatives. Asia Pacific region has emerged as a leading market due to its abundance of vendors developing robust and innovative machine learning solutions. With the Banking, Financial Services, and Insurance (BFSI) industry in the region expected to see significant growth in deploying security services, major companies are targeting this area to expand their operations. Moreover, Asian countries are at the forefront of advanced technologies and trends such as autonomous driving, artificial intelligence, e-health, and fintech. The region's digitalization landscape is diverse, with varying levels of readiness for capitalization, digital transformation, and regulatory capacities across different countries.

Key Automated Machine Learning Company Insights

Some key companies in the automated machine learning market are Akamai Technologies and Amazon Web Services, Inc.

• Akamai Technologies leads the automated machine learning market with its extensive global CDN infrastructure, enabling ultra-fast, secure, and reliable video delivery. Its advanced media compression, adaptive bitrate streaming, and real-time analytics optimize viewer experience. Akamai Technologies’ scalability and partnerships with major OTT platforms strengthen its leadership in global content distribution.

• Amazon Web Services, Inc. dominates through its powerful cloud-based video processing and streaming solutions. Its scalable infrastructure supports live and on-demand content processing, encoding, and delivery globally. AI-driven analytics, automation, and integration across Amazon Web Services, Inc. enables cost-efficient, high-quality video workflows, making it a preferred choice for broadcasters and OTT providers. 

Key Automated Machine Learning Companies:

The following key companies have been profiled for this study on the automated machine learning market.

• Amazon Web Services, Inc.

• Google LLC

• Microsoft

• DataRobot, Inc.

• H2O.ai.

• Databricks

• Oracle

• Alibaba Cloud

• Akkio Inc.

• Clarifai, Inc.

Competitive Benchmarking

Recent Developments

• In June 2025, Nordic Semiconductor, low-power wireless communication solutions provider, acquired Neuton.AI Inc., a provider of AutoML tools. This strengthens Nordic Semiconducto’s edge in deploying tiny, efficient AutoML models on resource-constrained devices (edge/IoT). The deal complements Nordic Semiconducto’s earlier acquisition of Atlazo, a semiconductor company and positions them to better support embedded ML use cases.

• In February 2025, IBM Corporation acquired DataStax, a company specializing in AI and data management solutions. This acquisition is expected to enhance IBM Corporation’s watsonx product lineup, enabling faster adoption of generative AI and helping businesses extract meaningful insights from extensive unstructured datasets.

• In May 2024, leveraging over two decades of collaboration, IBM Corporation and Adobe are providing clients with the expertise and technology to fully utilize Generative AI in marketing, content creation, and brand management. This is accomplished through a distinctive partnership that encompasses both technology solutions and consulting services, fostering collaborative innovation across hybrid cloud infrastructure, data utilization, applications, and a diverse Generative AI strategy.

Automated Machine Learning Market Report Scope

Global Automated Machine Learning Market Report Segmentation

This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2021 to 2033. For this study, Grand View Research has segmented global automated machine learning market report based on offering, enterprise size, deployment, application, vertical, and region.

• Offering Outlook (Revenue, USD Billion, 2021 - 2033)

  • Solution

  • Services

• Deployment Outlook (Revenue, USD Billion, 2021 - 2033)

  • On-Premises

  • Cloud

• Enterprise Size Outlook (Revenue, USD Billion, 2021 - 2033)

  • Large Enterprises

  • SMEs

• Application Outlook (Revenue, USD Billion, 2021 - 2033)

  • Data Processing

  • Feature Engineering

  • Model Selection

  • Hyperparameter Optimization Tuning

  • Model Ensembling

  • Others

• Vertical Outlook (Revenue, USD Billion, 2021 - 2033)

  • BFSI

  • Retail & E-Commerce

  • Healthcare

  • Government & Defense

  • Manufacturing

  • Media & Entertainment

  • Automotive & Transportation

  • IT & Telecommunications

  • Others

• Regional Outlook (Revenue, USD Billion, 2021 - 2033)

  • North America

    • U.S.

    • Canada

    • Mexico

  • Europe

    • Germany

    • UK

    • France

  • Asia Pacific

    • China

    • Japan

    • India

    • South Korea

    • Australia

  • Latin America

    • Brazil

  • Middle East and Africa (MEA)

    • UAE

    • KSA

    • South Africa

Research Methodology

The automated machine learning market figures in this report are based on a proven research process that combines executive interviews with secondary research from proprietary databases, company filings, and recognized regulatory and institutional sources. Market size is built through value-chain sizing - reconciling supply-side and demand-side estimates - and triangulated with bottom-up and top-down approaches. Every estimate passes multiple levels of expert validation before publication, with each automated machine learning segment quantified using the revenue-capture definitions in the table below.

Segment Definition

Estimation Model

Delivered Customizations

This report has been delivered with the following In-depth customizations