Automated Machine Learning (AutoML) Market Size - By Offering (Solution, Services [Consulting, Integration, Deployment]), By Deployment Mode (On-Premises, Cloud), By Enterprise Size (SME, Large enterprise), By Application, By End-User & Forecast, 2024 - 2032

Automated Machine Learning Market Size

Automated Machine Learning Market size was valued at USD 1.4 billion in 2023 and is estimated to register a CAGR of over 30% between 2024 and 2032, propelled by intensified R&D efforts. As organizations strive to harness the power of machine learning (ML) without extensive expertise, AutoML has emerged as a pivotal solution for democratizing AI capabilities. For instance, in July 2023, MIT researchers pioneered a groundbreaking solution BioAutoMATED, an automated machine-learning system simplified model selection and data preprocessing for slashing the time and effort involved.
 

With rising investments in AI-driven technologies, the need for efficient and accessible ML tools has become paramount. AutoML streamlines the ML pipeline for automating model selection, hyperparameter tuning, and feature engineering, thus reducing the barrier to entry for AI adoption. This surge in demand is evident across industries, from healthcare to finance, where data-driven insights are critical for innovation and competitiveness. With research continuing to enhance AutoML algorithms and frameworks, the automated machine learning market trajectory is expected to remain steady, promising broader accessibility and transformative potential in the AI landscape.
 

As studies increasingly highlight the efficacy of AutoML in simplifying the machine learning process, businesses are keen to capitalize on its benefits. AutoML boasts of a capacity to automate model selection, hyperparameter tuning, and feature engineering which not only reduces the barriers to entry for AI adoption but also enhances efficiency and accuracy. Thus, the rising number of studies on AutoML underscores its pivotal role in shaping the future of AI. For instance, in August 2023, a study showcased AutoML's potential to predict wireline logs and reservoir properties accurately for offering efficiency and reducing carbon emissions by eliminating manual analysis.
 

Moreover, the scarcity of data science expertise is posing a critical bottleneck in the organizational efforts to leverage ML effectively. As the demand for data-driven insights continues to soar, the shortage of skilled data scientists is exacerbating the challenge of building and deploying ML models. To that end, AutoML plays a pivotal role to address this gap by automating key aspects of the ML pipeline. By streamlining processes, such as model selection, hyperparameter tuning, and feature engineering, AutoML is empowering individuals without specialized skills to develop and deploy ML models efficiently. This democratization of ML capabilities is not only accelerating its adoption but is also reducing reliance on a limited pool of expert talent.
 

While the AutoML market is experiencing rapid growth, the lack of interpretability and transparency in AutoML models may restrict growth to some degree. As these systems automate complex processes, understanding how decisions are made has become challenging, further raising concerns about accountability and trust. Additionally, AutoML tools may struggle with handling highly specialized or niche datasets, limiting their applicability across diverse domains.
 

Automated Machine Learning Market Trends

The AutoML industry is expected to further experience significant growth, driven by rising applications and research in the medical field. As healthcare providers and researchers recognize the potential of AutoML in revolutionizing patient care and medical research, there is a surge in demand for AI-driven solutions tailored to healthcare challenges. AutoML offers the ability to automate complex machine learning tasks, such as model selection and feature engineering to streamline the development of predictive models for disease diagnosis, treatment optimization, and drug discovery.
 

Furthermore, the ongoing research in AutoML-specific methodologies for medical data analysis is expanding its scope and enhancing its accuracy in healthcare applications. These trends will signal a promising future for AutoML in transforming medical practices and improving patient outcomes. To cite an instance, in August 2023, a study was released for examining the suitability and efficacy of AutoML for prospective uses in diagnostic neuroradiology. The objective was to assess the feasibility and merits of employing AutoML models vis-à-vis traditional machine learning models.
 

Automated Machine Learning Market Analysis

Based on offering, the automated machine learning market is divided into solution and service. The solution segment dominates the market in 2023 and is projected to exceed USD 10 billion by 2032.  As companies seek efficient and accessible AI solutions, AutoML has emerged as a pivotal offering for streamlining the machine learning process without requiring extensive expertise.
 

AutoML solutions encompass a range of features, from automated model selection to hyperparameter tuning for catering to organizations of all sizes and industries. With the promise of democratizing AI capabilities and accelerating time-to-insight, the demand for AutoML solutions will continue to soar, fueled by the need for scalable, cost-effective, and user-friendly machine learning solutions.
 

Based on deployment mode, the automated machine learning market is categorized into cloud and on-premises. The cloud segment held a major market share of around 66% in 2023. As businesses increasingly migrate their operations to the cloud, the appeal of AutoML solutions hosted on cloud platforms is growing exponentially. Cloud deployment offers scalability, flexibility, and accessibility, enabling organizations to leverage AutoML capabilities without the need for extensive infrastructure or specialized expertise.
 

Moreover, cloud-based AutoML solutions facilitate seamless integration with existing workflows and data sources for accelerating time-to-value and enhancing competitiveness. This surge in demand for cloud-based AutoML will underscore its pivotal role in democratizing AI while driving innovations across industries.
 

North America dominated the global automated machine learning market with a share of over 37% in 2023. The thriving tech ecosystem across the region is fostering innovations, further creating a fertile ground for AutoML applications across various sectors. With the shortage of skilled data scientists and the growing need for AI-driven insights, several North American businesses are turning to AutoML to streamline the machine learning process. Moreover, the strong inclination towards automation and efficiency is amplifying the appeal of AutoML solutions to offer accessible and scalable AI capabilities.
 

Automated Machine Learning Market Share

Alphabet Inc. and Amazon Web Services, Inc. hold a significant market share of over 15% in the automated machine learning (AutoML) industry. These market players are essaying partnership-based strategies along with technological advances to sustain the rising market competition. Through dedicated R&D, they are tailoring to AutoML offerings to meet the unique needs of their clientele. Strong commitment to innovation and customer satisfaction is also positioning these firms at the forefront of satisfying the growing demand for efficient and accessible AI solutions.
 

Automated Machine Learning Market Companies

Major companies operating in the automated machine learning (AutoML) industry are:

• Alphabet Inc.
• Alteryx
• Amazon Web Services, Inc.
• Dataiku
• DataRobot, Inc.
• Feature Labs
• H2O.ai.
• IBM Corporation
• Microsoft
• TIBCO Software Inc.
   

Automated Machine Learning Industry News

• In September 2023, Fujitsu Limited, in collaboration with the Linux Foundation, officially introduced its automated machine learning and AI fairness technologies as open source software (OSS) in anticipation of the "Open Source Summit Europe 2023" event.
   
• In July 2023, DiamiR Biosciences, a pioneer in non-invasive blood-based diagnostic tests for brain health and various diseases, disclosed a partnership with JADBio. This collaboration aimed to leverage JADBio's AutoML Platform and Services to craft predictive models.
   

The automated machine learning (AutoML) market research report includes in-depth coverage of the industry, with estimates & forecast in terms of revenue (USD Million) from 2021 to 2032, for the following segments:

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Market, By Offering

• Solutions
• Services
  • Consulting
  • Integration
  • Deployment

Market, By Deployment Mode

• Cloud
• On-premises

Market, By Enterprise Size

• SME
  • Solutions
  • Services
    • Consulting
    • Integration
    • Deployment 
• Large enterprise
  • Solutions
  • Services
    • Consulting
    • Integration
    • Deployment

Market, By Application

• Data Processing
• Feature engineering
• model selection
• Hyperparameter optimization & tuning
• Model ensemble
• Others

Market, By End-user

• IT & Telecommunications
• BFSI
• Retail
• Automotive
• Media & Entertainment
• Others

The above information is provided for the following regions and countries:

• North America
  • U.S.
  • Canada
• Europe
  • UK
  • Germany
  • France
  • Russia
  • Italy
  • Spain
  • Rest of Europe
• Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • ANZ
  • Southeast Asia
  • Rest of Asia Pacific 
• Latin America
  • Brazil
  • Mexico
  • Argentina
  • Rest of Latin America 
• MEA
  • UAE
  • South Africa
  • Saudi Arabia
  • Rest of MEA