자동 머신러닝 : 시장 점유율 분석, 산업 동향, 통계, 성장 예측(2025-2030년)

The Automated Machine Learning Market size is estimated at USD 2.59 billion in 2025, and is expected to reach USD 15.98 billion by 2030, at a CAGR of 43.9% during the forecast period (2025-2030).

Machine learning (ML) is a subfield of artificial intelligence (AI) that enables training algorithms to make classifications or predictions through statistical methods, uncovering critical insights within data mining projects. These insights drive decision-making within applications and businesses, ideally impacting key growth metrics. Skilled professionals must develop these solutions since they revolve around algorithms, models, and computational complexity.

Key Highlights

• Machine learning (ML) has become an essential component. On the other hand, building high-performance machine-learning applications necessitates highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to decrease data scientists' needs by allowing domain experts to automatically construct machine learning applications without considerable statistics and machine learning knowledge.
• Due to the increasing adoption of IoT, automation, and cloud-based services, investment in the market has been rising. The solution allows SMEs and enterprises to outsource everything needed to improve data quality, security, safety, and readiness for machine learning and avoid the cost and challenges of employing a data science resource. This service is also supported by Calligo's Data Insights Platform, which is purpose-built for machine learning workloads. For instance, in January 2024, Google Cloud and Hugging Face Announced a Strategic Partnership to Accelerate Generative AI and ML Development. This collaboration will allow developers to utilize Google Cloud's infrastructure for all Hugging Face services, enabling training and serving of Hugging Face models on Google Cloud.
• Some firms have shifted to AutoML to automate internal procedures, particularly the creation of ML models, such as Facebook and Google. Asimo is Facebook's AutoML developer, which automatically generates improved versions of current models. Google also released AutoML tools to automate the process of discovering optimization models and designing machine learning algorithms. Google launched "Cloud AutoML," a product that allows businesses with limited Machine Learning (ML) expertise to build high-quality, custom artificial intelligence (AI) models to enhance Google's products and services. "Cloud AutoML" lets businesses and developers train custom vision models for their use cases. Such innovations by the companies will drive the market.
• The AutoML market is expected to experience significant growth, driven by rising applications and research in the medical field. As AutoML revolutionizes patient care and medical research, there is a surge in demand for AI-driven solutions tailored to healthcare challenges. AutoML can automate complex machine learning tasks, such as model selection and feature engineering, to streamline the development of predictive models for illness diagnosis, treatment optimization, and drug discovery.
• Machine learning (ML) is increasingly used in many applications, but there needs to be more machine learning experts to support this growth adequately. With automated machine learning (AutoML), the purpose is to make machine learning more accessible. Therefore, experts should be able to deploy more machine learning systems, and less expertise would be required to work with AutoML than when working with ML directly. However, the adoption of technology still needs to be deeper, restraining the market's growth.
• The adoption of AI witnessed an increase post-COVID-19 as companies leveraged intelligent solutions for automating their business processes. This trend is anticipated to continue over the coming years, further driving the adoption of AI in organizational processes.

Automated Machine Learning Market Trends

The BFSI Segment is Driving Market Growth

• AI and ML technologies are increasingly adopted in the banking, financial services, and insurance (BFSI) industry to enhance operational efficiency and improve the consumer experience. As data gains more attention, the demand for machine learning BFSI applications grows. Automated machine learning can produce accurate and rapid results with enormous data, affordable processing power, and economical storage.
• Machine learning (ML)-powered solutions also enable finance firms to replace manual labor by automating repetitive operations through intelligent process automation, increasing corporate productivity for chatbots, paperwork automation, and employee training gamification, among others. Machine learning is expected to be used to automate financial processes.
• After the pandemic, financial institutions showed increased interest in reaching and assisting customers through digital channels. Various digital solutions, including chatbots, account opening and management support, and technical assistance, witnessed a surge in adoption within the finance sector, especially in fintech corporations like Posh. Tech, Spixii, and numerous others now provide intelligent chatbots designed to facilitate essential customer-facing functions for banks.
• HDFC Bank uses an AI-based chatbot, "Eva," built by Bengaluru-based Senseforth AI Research. Since its launch in March this year, Eva (which stands for Electronic Virtual Assistant) has addressed over 2.7 million client queries, interacted with over 530,000 unique users, and held 1.2 million conversations. Deutsche Bank announced a multi-year innovation partnership with NVIDIA to accelerate the use of artificial intelligence (AI) and machine learning (ML) in the finance sector.
• Banks must improve their services to offer better customer service with the rising pressure in managing risk and increasing governance and regulatory requirements. The rising number of bank fraud cases is expected to increase the adoption of AI and ML. Some fintech brands have been increasingly using AI and ML in different applications across multiple channels to leverage available client data and predict how customers' needs are evolving, which fraudulent activities have the highest possibility to attack a system, and what services will prove beneficial, among others.
• In FY 2023, the Reserve Bank of India (RBI) reported more than 13 thousand bank fraud cases across India, an increase compared to the previous year. It turned around the previous decade's trend. Such increases in bank fraud may further generate market demand.

North America to Hold a Significant Market Share

• North America is expected to hold a substantial share of the market owing to the robust innovation ecosystem, fueled by strategic federal investments into advanced technology, complemented by the existence of visionary scientists and entrepreneurs coming together from across the world and recognized research institutions, driving the development of automated machine learning (AutoML).
• Various governments, including state and local governments, handle enormous quantities of citizen data, which used to be stored on paper and processed manually. However, as artificial intelligence (AI) and machine learning technologies provide faster and more accurate data-gathering and processing methods, governments can focus on more complex and long-term social and cultural issues. Further, an increase in commercial applications for federated ML is expected to drive the demand for AutoML.
• According to the Government of Canada, artificial intelligence (AI) technologies promise to enhance how the Canadian government serves its citizens. As the government investigates the usage of artificial intelligence in government programs and services, it ensures that clear values, ethics, and rules guide it.
• While the United States is trying to establish AutoML supremacy, Canada is also gearing up for such developments. For instance, in April 2023, ePayPolicy launched Payables Connect, the latest addition to its insurance payment and reconciliation products suite. It leverages ePay's existing integration and machine learning technology to automate the reconciliation, design, and payment of due payables completely.
• Though Canada is still in the initial phase of deploying automated machine learning across various industries, some factors, including the rising need to automate the finance sector and the emerging educational interest among students, are expected to drive market growth.
• The region's AutoML market is changing due to the cloud; serverless computing allows creators to get ML applications up and running quickly. For instance, in October 2023, according to AWS, US cloud computing infrastructure investment exceeded USD 108 billion.
• Moreover, many organizations of different sizes are transforming from traditional to digital modes of business. This transformation creates a hybrid cloud market because of the benefits, like reduced total cost of ownership (TCO), high security, flexibility, and agility. IBM stated that 89% of IT leaders are expected to move business-critical workloads to the cloud, and the growth in digitization drives all. Such expansion in cloud solutions may further propel the market's growth in the region.

Automated Machine Learning Industry Overview

The global automated machine learning market exhibits moderate fragmentation, with numerous players meeting market demands. The competition is driven by the influx of new entrants, prompting existing participants to devise strategies for expanding their customer base. This dynamic landscape also spurs innovation as existing market players strive to develop cutting-edge products. Notable market leaders include Datarobot Inc., Amazon Web Services Inc., dotData Inc., IBM Corporation, and Dataiku.

• February 2024: Wipro Limited, a significant technology services and consulting corporation, announced the launch of Wipro Enterprise Artificial Intelligence (AI)-Ready Platform, a new service allowing clients to create enterprise-level, fully integrated, and customized AI environments. The Wipro Enterprise AI-Ready Platform leverages the IBM Watsonx AI and data platform, including watsonx.data, watsonx.ai, and watsonx. Governance and AI assistants offer clients an interoperable service that accelerates AI adoption. This unique service enhances operations with capabilities spanning tools, large language models (LLMs), streamlined processes, and strong governance. It also lays the foundation for future enterprise analytic solutions to be built on watsonx.data and AI.
• May 2024: Snapchat announced a series of the latest augmented reality (AR) and machine learning (ML) tools developed to help brands and advertisers provide users with interactive experiences. The company had been investing in automation and ML to make it faster and easier for brands to create AR try-on assets.
• September 2023: Fujitsu Limited and the Linux Foundation announced the launch of Fujitsu's automated machine learning and AI fairness technologies as open-source software (OSS) ahead of the "Open Source Summit Europe 2023," running in Bilbao, Spain, from September 2023. The two projects were expected to offer users access to software that automatically generates code for unique machine-learning models and a technology that addresses latent biases in training data.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET DYNAMICS

• 4.1 Market Drivers
  • 4.1.1 Increasing Demand for Efficient Fraud Detection Solutions
  • 4.1.2 Growing Demand for Intelligent Business Processes
• 4.2 Market Restraints
  • 4.2.1 Slow Adoption of Automated Machine Learning Tools
• 4.3 Industry Value Chain Analysis
• 4.4 Industry Attractiveness - Porter's Five Forces Analysis
  • 4.4.1 Threat of New Entrants
  • 4.4.2 Bargaining Power of Buyers
  • 4.4.3 Bargaining Power of Suppliers
  • 4.4.4 Threat of Substitute Products
  • 4.4.5 Intensity of Competitive Rivalry
• 4.5 Impact of Key Macroeconomic Trends on the Market

5 MARKET SEGMENTATION

• 5.1 By Solution
  • 5.1.1 Standalone or On-Premise
  • 5.1.2 Cloud
• 5.2 By Automation Type
  • 5.2.1 Data Processing
  • 5.2.2 Feature Engineering
  • 5.2.3 Modeling
  • 5.2.4 Visualization
• 5.3 By End User
  • 5.3.1 BFSI
  • 5.3.2 Retail and E-Commerce
  • 5.3.3 Healthcare
  • 5.3.4 Manufacturing
  • 5.3.5 Other End Users
• 5.4 By Geography
  • 5.4.1 North America
    • 5.4.1.1 United States
    • 5.4.1.2 Canada
  • 5.4.2 Europe
    • 5.4.2.1 United Kingdom
    • 5.4.2.2 Germany
    • 5.4.2.3 France
    • 5.4.2.4 Rest of Europe
  • 5.4.3 Asia-Pacific
    • 5.4.3.1 China
    • 5.4.3.2 Japan
    • 5.4.3.3 South Korea
    • 5.4.3.4 Rest of Asia-Pacific
  • 5.4.4 Rest of the World

6 COMPETITIVE LANDSCAPE

• 6.1 Company Profiles
  • 6.1.1 DataRobot Inc.
  • 6.1.2 Amazon web services Inc.
  • 6.1.3 dotData Inc.
  • 6.1.4 IBM Corporation
  • 6.1.5 Dataiku
  • 6.1.6 SAS Institute Inc.
  • 6.1.7 Microsoft Corporation
  • 6.1.8 Google LLC (Alphabet Inc.)
  • 6.1.9 H2O.ai
  • 6.1.10 Aible Inc.

7 INVESTMENT ANALYSIS

8 FUTURE OF THE MARKET