Automated Machine Learning (AutoML) Market Size, Share, Analysis, Trends, Growth, 2032

Automated Machine Learning (AutoML) Market

Automated Machine Learning (AutoML) Market By Deployment (On-Premises and Cloud), By Application (Model Selection, Data Processing, Hyperparameter Optimization & Tuning, Feature Engineering, and Others), By Offering (Service and Solution), By Enterprise Size (Large Enterprises and Small & Medium Enterprises), and By Region - Global and Regional Industry Overview, Market Intelligence, Comprehensive Analysis, Historical Data, and Forecasts 2024 - 2032

Category: Technology & Media Report Format : PDF Pages: 227 Report Code: ZMR-8943 Published Date: Nov-2024 Status : Published
Market Size in 2023 Market Forecast in 2032 CAGR (in %) Base Year
USD 2.54 Billion USD 58.95 Billion 41.82% 2023

Automated Machine Learning (AutoML) Market

Automated Machine Learning (AutoML) Industry Prospective:

The global automated machine learning (AutoML) market size was worth around USD 2.54 billion in 2023 and is predicted to grow to around USD 58.95 billion by 2032, with a compound annual growth rate (CAGR) of roughly 41.82% between 2024 and 2032.

Global Automated Machine Learning (AutoML) Market SizeRequest Free Sample

Automated Machine Learning (AutoML) Market: Overview

Automated machine learning (AutoML) refers to the process of automating the task of developing machine learning models. AutoML tools are specially designed to cater to the needs of developers, analysts, or data scientists who do not have expertise in machine learning (ML) model development. The tasks related to ML programming and development are excessively time-consuming.

Moreover, it requires significant brainstorming sessions to develop a successful, high-quality model. This process is taken care of with the implementation of AutoML tools designed to allow the building of machine learning models with higher efficiency, scalability, and productivity without compromising the quality of the model.

The demand for automated machine learning is witnessed across industries, including healthcare, real estate, advertisement & marketing, banking, financial services, & insurance (BFSI), and other major sectors.

The growing development of Artificial Intelligence (AI)-driven technologies will fuel the demand for advanced AutoML systems.

However, the high cost of technology and integration challenges will limit the automated machine learning industry’s expansion trend.

Key Insights:

  • As per the analysis shared by our research analyst, the global automated machine learning (AutoML) market is estimated to grow annually at a CAGR of around 41.82% over the forecast period (2024-2032)
  • In terms of revenue, the global automated machine learning (AutoML) market size was valued at around USD 2.54 billion in 2023 and is projected to reach USD 58.95 billion by 2032.
  • The automated machine learning market is projected to grow at a significant rate due to the growing investments in AI-driven technologies.
  • Based on deployment, the cloud segment is growing at a high rate and will continue to dominate the global market, as per industry projections.
  • Based on the offering, the solution segment is anticipated to command the largest market share.
  • Based on region, North America is projected to dominate the global market during the forecast period.

Automated Machine Learning (AutoML) Market: Growth Drivers

Growing investments in AI-driven technologies will fuel the market demand rate

The global automated machine learning (AutoML) market is expected to benefit from the increasing demand for AI-driven technologies across the globe.

Machine learning and Artificial Intelligence are separate technologies, but the former is a subset of the AI systems. Machine learning is categorized as a variant of AI in which machines can be taught to learn and improve using data and experience without programming. ML depends on algorithms for analyzing large volumes of data and making predictions based on data analysis.

Automated machine learning tools can be leveraged by companies investing in AI technologies to reduce the amount of resources spent on developing and implementing the technology.

AutoML systems can easily automate and simplify the tasks associated with the ML process, thus saving significant resources from being wasted.

According to extensive market research, AI investments have surged in recent times. The peak for investments was observed between 2022 and 2023, with the total amount reaching over USD 2.9 billion.

As per official data, AI-based investments may contribute around 4% of the US gross domestic product (GDP) in the coming years, opening doors for further growth in the AutoML sector.

Rising use of the technology in the manufacturing sector will promote further market expansion

Automated machine learning tools are witnessing growing demand in the manufacturing sector. Technology plays a crucial role in maintaining product quality in the modern manufacturing sector.

Moreover, it further assists in demand forecasting, predictive maintenance of machines, and ensuring supply chain optimization.

The manufacturing sector is witnessing a steady shift in business operations as more companies are leveraging technological tools to improve final monetary and non-monetary outcomes.

The global automated machine learning (AutoML) market players must focus on the rising investments in technology-oriented manufacturing industries.

Automated Machine Learning (AutoML) Market: Restraints

High cost of AutoML technology and integration complexities will limit the industry’s expansion trend

The global industry for automated machine learning (AutoML) will be affected by the high cost of the technology in the initial stages.

The development or implementation of AutoML systems requires extensive investments and the construction of supporting technological infrastructure.

For instance, according to official reports, major AutoML solution providers charge between $0.10 and $5 per hour. Additionally, licensing charges for AutoML tools reach over USD 10,000. The complexities associated with integrating AutoML tools with legacy systems may further impact the market growth trend.

Automated Machine Learning (AutoML) Market: Opportunities

Rising launch of new solutions will generate more growth opportunities during the projection period

The global automated machine learning (AutoML) market is projected to generate growth opportunities due to the rising delivery of new solutions offering industry-specific and customized solutions.

For instance, in May 2024, SensiML™ Corporation, one of the world’s leading players in the AI/ML software for the Internet of Things (IoT) technology, announced the launch of a new solution that can revolutionize the TinyML® industry.

According to official reports, it is the first solution of its kind to offer 100% open-source development, delivering an Analytics Studio application. The company aims to promote creativity and innovation along with greater AI code transparency by launching an open-source offering.

In April 2024, a German company in the control and automation technology industry announced the launch of a new AutoML tool at the Hannover Messe 2024, an industrial trade fair.

In addition, the market players can benefit from the growing acceptance of cloud technology as businesses are more open to adopting advanced systems for sustainable growth in the competitive economy.

Automated Machine Learning (AutoML) Market: Challenges

Limited availability of skilled employees and lack of quality data will challenge the market expansion trends

The global automated machine learning (AutoML) industry is projected to be challenged by the lack of availability of skilled employees to leverage the complete offerings of AutoML tools.

Additionally, most companies struggle with a lack of good-quality data to train ML models, further impacting the market adoption rate.

Automated Machine Learning (AutoML) Market: Segmentation

The global automated machine learning market is segmented based on deployment, application, offerings, enterprise size, and region.

Based on the deployment, the global market segments are on-premises and cloud. In 2023, the highest growth witnessed in the cloud segment held control over more than 66.05% of the total share.

The growing migration among businesses toward cloud technology is fueling the segmental growth rate. On-premises segment requires extensive investments and maintenance limiting the industry’s expansion trends.

Based on application, the global automated machine learning industry is divided into model selection, data processing, hyperparameter optimization & tuning, feature engineering, and others.

Based on the offering, the global automated machine learning market divisions are service and solution. In 2023, the highest revenue-generating segment was in the solution area. It is likely to cross over USD 10.01 billion in terms of revenue by the end of the projection period.

The rising demand for highly sophisticated and easy-to-apply ML solutions is fueling the segmental growth rate. The service sector may register significant revenue due to increased AI investments.

Based on enterprise size, the automated machine learning industry segments are large enterprises and small & medium enterprises.

Automated Machine Learning (AutoML) Market: Report Scope

Report Attributes Report Details
Report Name Automated Machine Learning (AutoML) Market
Market Size in 2023 USD 2.54 Billion
Market Forecast in 2032 USD 58.95 Billion
Growth Rate CAGR of 41.82%
Number of Pages 227
Key Companies Covered IBM Watson Studio, Google Cloud AutoML, Neural Designer, Salesforce Einstein, Arimo (acquired by Panasonic), Domino Data Lab, Amazon SageMaker Autopilot, SAP Analytics Cloud, Microsoft Azure Machine Learning, Zegami, RapidMiner, DataRobot, AWS SageMaker, SigOpt, H2O.ai, BigML, NVIDIA, Knime, Alteryx, Tecton., and others.
Segments Covered By Deployment, By Application, By Offering, By Enterprise Size, and By Region
Regions Covered North America, Europe, Asia Pacific (APAC), Latin America, Middle East, and Africa (MEA)
Base Year 2023
Historical Year 2018 to 2022
Forecast Year 2024 - 2032
Customization Scope Avail customized purchase options to meet your exact research needs. Request For Customization

Automated Machine Learning (AutoML) Market: Regional Analysis

North America to register the highest revenue during the forecast period

The global automated machine learning (AutoML) market will be led by North America during the projection period. In 2023, it held control over 37.05% of the global revenue, with the US generating the highest revenue in the regional market.

The presence of major international companies, such as Amazon, Microsoft, Google, RapidMiner, and others, is expected to fuel the regional demand rate in the coming years.

In September 2024, US-based Qlik, a leading player in the data analytics, integration, and AI industry, announced the expansion of its AutoML offerings. The new capabilities will allow analytics to develop high-performance machine learning models.

The product is available with 100% integration into the Qlik cloud. Between February 2024 and April 2024, the US Food and Drug Administration (FDA) Office of Digital Transformation's (ODT's) precisionFDA hosted the Automated Machine Learning (AutoML) App-a-thon, inviting companies to unlock the potential of AutoML in healthcare-related applications.

In September 2023, the Linux Foundation partnered with Japan’s AI company Fujitsu for the launch of the latter’s AutoML and AI technologies as open-source software (OSS).

Automated Machine Learning (AutoML) Market: Competitive Analysis

The global automated machine learning (AutoML) market is led by players like:

  • IBM Watson Studio
  • Google Cloud AutoML
  • Neural Designer
  • Salesforce Einstein
  • Arimo (acquired by Panasonic)
  • Domino Data Lab
  • Amazon SageMaker Autopilot
  • SAP Analytics Cloud
  • Microsoft Azure Machine Learning
  • Zegami
  • RapidMiner
  • DataRobot
  • AWS SageMaker
  • SigOpt
  • H2O.ai
  • BigML
  • NVIDIA
  • Knime
  • Alteryx
  • Tecton.

The global automated machine learning (AutoML) market is segmented as follows:

By Deployment

  • On-Premises
  • Cloud

By Application

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

By Offering

  • Service
  • Solution

By Enterprise Size

  • Large Enterprises
  • Small & Medium Enterprises

By Region

  • North America
    • The U.S.
    • Canada
  • Europe
    • France 
    • The UK
    • Spain
    • Germany
    • Italy
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Southeast Asia
    • Rest of Asia Pacific
  • Latin America
    • Brazil
    • Mexico
    • Rest of Latin America
  • Middle East & Africa
    • GCC
    • South Africa
    • Rest of Middle East & Africa

Table Of Content

Methodology

FrequentlyAsked Questions

Automated machine learning (AutoML) refers to the process of automating the task of developing machine learning models.

The global automated machine learning (AutoML) market is expected to benefit from the increasing demand for AI-driven technologies across the globe.

According to study, the global automated machine learning (AutoML) market size was worth around USD 2.54 billion in 2023 and is predicted to grow to around USD 58.95 billion by 2032.

The CAGR value of the automated machine learning (AutoML) market is expected to be around 41.82% during 2024-2032.

The global automated machine learning (AutoML) market will be led by North America during the projection period.

The global automated machine learning (AutoML) market is led by players like IBM Watson Studio, Google Cloud AutoML, SAP Analytics Cloud, Microsoft Azure Machine Learning, Zegami, RapidMiner, DataRobot, AWS SageMaker, SigOpt, H2O.ai, BigML, NVIDIA, Knime, Alteryx, and Tecton.

The report explores crucial aspects of the automated machine learning (AutoML) market including a detailed discussion of existing growth factors and restraints while also browsing future growth opportunities and challenges that impact the market.

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