Automated Machine Learning (AutoML) Market projected to reach USD 10.38 Billion by 2030, growing at a CAGR of 36.76% during the forecast period of 2023-2030 – professed by MarketDigits in its recent study

The Global Automated Machine Learning (AutoML) Market was valued USD 1.16 Billion in 2023 and projected to reach USD 10.38 Billion by 2030, growing at a CAGR of 36.76% during the forecast period of 2023-2030

Richmond, Dec. 19, 2023 (GLOBE NEWSWIRE) — According to a research report Automated Machine Learning (AutoML) Market, By Offerings (Platform, Service), Deployment type (On-Premises, Cloud), Application (Fraud Detection, Sales & Marketing Management, Medical Testing, Transport Optimization), Vertical (BFSI, IT & Telecom, Healthcare, Government, Retail, Manufacturing, Others) and Region – Global Forecast to 2030.

Global Automated Machine Learning (AutoML) Market Report Scope:

Report Details
Market size value in 2023 USD 1.16 Billion
Market size value in 2030 USD 10.38 Billion
CAGR (2023-2030) 36.76%
Forecast Period 2023–2030
Historic Data 2019
Forecast Units Value (USD Million/USD Billion)
Segments Covered Offering, Deployment type, Application, Vertical and Region
Geographies Covered North America, Europe, Asia Pacific, and RoW
  • IBM
  • Oracle
Sample of Companies Covered
  • Microsoft
  • ServiceNow
  • Google

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TOC Covers in Depth & Breath on Automated Machine Learning (AutoML) Market
170 – Market Data Tables
65 – List of Figures
225 – Pages

The report includes Vendor Assessment (Company Profiles, Market Positioning, Strategies, Recent Developments, Capabilities & Product Offerings / Mapping), Technology Assessment (Developments & Economic Impact), Partner & Customer Ecosystem (Product Services, Proposition & Key Features) Competitive Index & Regional FootPrint by MarketDigits.

Market Overview

The Automated Machine Learning (AutoML) market is experiencing significant growth and evolution, driven by the escalating demand for sophisticated machine learning solutions across diverse industries. AutoML, a subset of artificial intelligence, is designed to streamline and automate the complex process of developing machine learning models, making it accessible to a broader audience with varying levels of technical expertise. The market is witnessing increased adoption as organizations recognize the importance of data-driven decision-making in gaining a competitive edge. The rising complexity of machine learning tasks, coupled with a shortage of skilled data scientists, further propels the demand for AutoML solutions. Key players in the market are innovating to provide user-friendly platforms that automate tasks such as data preprocessing, feature engineering, model selection, and hyperparameter tuning. With the democratization of machine learning capabilities, the AutoML market is poised for continued expansion, offering businesses a powerful tool to harness the potential of data analytics for strategic insights and operational efficiency.

Major vendors in the global Automated Machine Learning (AutoML) Market:-

  • IBM
  • Oracle
  • Microsoft
  • ServiceNow
  • Google
  • Baidu
  • AWS (Amazon Web Services)
  • Alteryx
  • Salesforce
  • Altair
  • Teradata
  • DataRobot
  • BigML
  • Databricks
  • Dataiku
  • Alibaba Cloud
  • Appier
  • Squark
  • Aible
  • Datafold
  • Akkio
  • Valohai
  • dotData
  • Qlik
  • Mathworks
  • HPE (Hewlett Packard Enterprise)
  • SparkCognition

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Increasing demand for data-driven decision-making

The increasing demand for data-driven decision-making is a key driver for the Automated Machine Learning (AutoML) market. As organizations across various industries recognize the critical role of data in gaining a competitive edge, there is a growing need for effective and efficient tools to harness the power of machine learning. AutoML addresses this demand by automating the end-to-end process of developing machine learning models, enabling individuals with varying levels of technical expertise to leverage the benefits of data-driven insights. The automation of tasks such as feature engineering, model selection, and hyperparameter tuning not only accelerates the model development process but also democratizes access to advanced analytics, making it more accessible to a broader range of professionals within an organization. This trend is expected to fuel the growth of the AutoML market as businesses increasingly prioritize data-driven decision-making for strategic advantages in today’s competitive landscape.

Market Dynamics


  • Growing adoption of cloud computing
  • Increasing Demand for Efficient Fraud Detection Solutions.


  • Developing AutoML solutions for reinforcement learning (RL)
  • Developing AutoML solutions for natural language processing (NLP)
  • Increasing Democratizing machine learning (ML).

AutoML for explainable AI (XAI)

In the Automated Machine Learning (AutoML) market, a noteworthy trend gaining momentum is the integration of explainable AI (XAI) capabilities. As organizations increasingly rely on machine learning models for critical decision-making, there is a growing emphasis on understanding and interpreting the outcomes of these models. Explainable AI addresses the need for transparency and interpretability in machine learning algorithms, enabling users to comprehend how a model reaches specific conclusions. AutoML platforms are incorporating XAI techniques to enhance the interpretability of the automated models they generate. This trend is driven by the recognition that, especially in regulated industries or mission-critical applications, the ability to explain model predictions is essential for gaining trust, ensuring compliance, and facilitating broader adoption of machine learning solutions. As a result, AutoML solutions equipped with explainable AI features are positioned to be a significant driver in the evolving landscape of automated machine learning.

North America dominates the market for Automated Machine Learning (AutoML) Market.

The North American AutoML market is anticipated for significant growth, driven by increased investments from the BFSI, retail, and healthcare sectors. Both the U.S. and Canada are expected to experience substantial inflows of capital into AI technology and machine learning, aiming to automate workflows and empower data scientists to focus on more intricate challenges. While the media, Information and Communication Technology (ICT) sector, and professional services are set to bolster investments in machine learning and AI, the manufacturing, mining, and utilities sectors may be in the early stages of adopting these technologies.

The coming year’s present compelling growth opportunities for AutoML companies, with end-users actively seeking new avenues in automation technologies to gain a competitive edge. According to the Government of Canada, there is a strong emphasis on exploring the potential of artificial intelligence in improving government services, with a commitment to ensuring clear values, ethics, and regulations guide its implementation.

Fraud Detection Segments Is Anticipated To Hold The Largest Market Share During The Forecast Period

Based on the Application segment categorized into Fraud Detection, Sales & Marketing, Management, Medical Testing, and Transport Optimization. The fraud detection segment is poised to hold a significant portion of the AutoML market, primarily driven by the demand for real-time monitoring of suspicious activities. The increasing focus on mitigating unauthorized use of financial services will fuel the adoption of AutoML solutions. The surge in online credit card fraud and the growing volume of transactions via wallets and mobile phones will further boost the need for AutoML tools in fraud detection. The fraud detection segment commands a substantial share in the Automated Machine Learning (AutoML) market due to its critical role in addressing the escalating challenges of unauthorized activities. The prominence of real-time monitoring for identifying and preventing suspicious transactions has become imperative for businesses, driving the adoption of AutoML solutions. As instances of unauthorized use of financial services continue to rise, there is a heightened need for sophisticated and efficient tools to bolster fraud prevention measures.

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