Automated Machine Learning Market To Reach $21,969.7 Million By 2030

Automated Machine Learning Market Growth & Trends

The global automated machine learning market size is expected to reach USD 21,969.7 million by 2030, and growing at a CAGR of 42.2% from 2024 to 2030, according to a new report by Grand View Research, Inc. The global market size expanding on the backdrop of the rising need for advanced fraud detection solutions. Data analysis techniques, including supervised neural networks, have become highly sought-after to detect fraud through forecasting, clustering, and classification.

Organizations are expected to invest in automated machine learning (AutoML) to boost customer trust and ensure compliance with laws. AutoML is an innate 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.

The cloud-based segment will exhibit notable growth due to the trend for custom ML models and the demand for scalability. Cloud-based AutoML has become trendier across businesses for image recognition, training, and managing models. Furthermore, some factors, such as faster turnaround time for the production-ready models, increased accuracy, and simple graphical user interface have encouraged organizations to invest in cloud automated machine learning.

Moreover, the fraud detection is significantly augmenting the market growth. The trend is mainly due to real-time monitoring of suspicious activity. A palpable rise to do away with the unauthorized use of financial services will further the need for AutoML solutions and services. An uptick in online credit card fraud and a soaring number of transactions through wallets and cell phones will further expedite the demand for AutoML tools for fraud detection.

Additionally, the healthcare sector will emphasize the expansion of AutoML solutions following the latter’s use in projecting disease progression, treatment planning, clinical information extraction, and patient care. Automated machine learning services could expand the application of ML algorithms in diabetes diagnosis and electronic health records (EHR), and Alzheimer’s diagnosis analysis. To illustrate, in December 2020, Google rolled out AutoML Entity Extraction for Healthcare and healthcare Natural Language API to help healthcare professionals assess and review medical documents in a scalable and repeatable way.

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Automated Machine Learning Market Report Highlights

• Based on offering, the service segment led the market and accounted for 52.4% of the global revenue in 2023. Automated Machine Learning (AutoML) 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.

• Automated machine learning 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.

• Based on enterprise size, the automated machine learning market is categorized into Small and Medium Enterprises (SMEs) and large enterprises. Large businesses are increasingly adopting cloud-based AutoML platforms and services. The scalable and cost-effective infrastructure of cloud platforms facilitates the training and deployment of machine learning models.

• 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.

• Based on deployment, 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.

• The Automated Machine Learning (AutoML) market streamlines 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.

Automated Machine Learning Market Segmentation

Grand View Research has segmented the global automated machine learning market based on offerings, enterprise size, deployment, application, vertical, and region:

AutoML Offering Outlook (Revenue, USD Million, 2017 - 2030)

• Solution

• Services

AutoML Enterprise Size Outlook (Revenue, USD Million, 2017 - 2030)

• SMEs

• Large Enterprises

AutoML Deployment Outlook (Revenue, USD Million, 2017 - 2030)

• Cloud

• On-premises

AutoML Application Outlook (Revenue, USD Million, 2017 - 2030)

• Data Processing

• Feature Engineering

• Model Selection

• Hyperparameter Optimization Tuning

• Model Ensembling

• Others

AutoML Vertical Outlook (Revenue, USD Million, 2017 - 2030)

• BFSI

• Retail & E-commerce

• Healthcare

• Government & Defense

• Manufacturing

• Media & Entertainment

• Automotive & transportation

• IT & Telecommunications

• Others

AutoML Regional Outlook (Revenue, USD Million, 2017 - 2030)

• North America

  • U.S.

  • Canada

• Europe

  • Germany

  • UK

  • France

• Asia Pacific

  • China

  • Japan

  • India

  • South Korea

  • Australia

• Latin America

  • Brazil

  • Mexico

• Middle East and Africa (MEA)

  • Kingdom of Saudi Arabia

  • UAE

  • South Africa

List of Key Players in the Automated Machine Learning (AutoML) Market

• IBM

• Oracle

• Microsoft

• ServiceNow

• Google LLC

• Baidu Inc.

• AWS

• Alteryx

• Salesforce

• Altair

• Teradata

• H2O.ai

• BigML

• Databricks

• Dataiku

• Alibaba Cloud