세계 자동 머신러닝(AutoML) 시장 : 시장 점유율 분석, 업계 동향과 통계, 성장 예측(2024-2029년)

The Automated Machine Learning Market size is estimated at USD 1.8 billion in 2024, and is expected to reach USD 11.12 billion by 2029, growing at a CAGR of 43.90% during the forecast period (2024-2029).

Key Highlights

• Machine learning (ML) is a subfield of artificial intelligence (AI) that enables training algorithms to make classifications or predictions through statistical methods, uncovering key insights within data mining projects. These insights drive decision-making within applications and businesses, ideally impacting key growth metrics. Since it revolves around algorithms, models, and computational complexity, skilled professionals must develop these solutions.
• Machine learning (ML) has become an essential component of many parts of the business. 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 knowledge of statistics and machine learning.
• The performance of automated machine learning has advanced due to data science and artificial intelligence improvements. Companies recognize the potential of this technology, and hence its adoption rate is likely to rise over the forecast period. Companies are selling automated machine learning solutions on a subscription basis, making it easier for customers to use this technology. Furthermore, it offers flexibility on a pay-as-you-go basis.
• Machine learning (ML) is increasingly used in many applications, but there are insufficient machine learning experts to support this growth adequately. With automated machine learning (AutoML), the aim is to make machine learning easier to use. Therefore, experts should be able to deploy more machine learning systems, and less expertise would be needed to work with AutoML than when working with ML directly. However, the technology adoption is still shallow, restraining the market's growth.
• The adoption of AI is witnessing an increase after the COVID-19 pandemic as companies move towards leveraging intelligent solutions for automating their business processes. This trend is expected to continue over the coming years, further driving the adoption of AI in organizational processes.

Automated Machine Learning (AutoML) Market Trends

BFSI to be the Largest End-user Industry

• In recent years, artificial intelligence (AI) and machine technologies have been increasingly adopted in the banking, financial services, and insurance (BFSI) industry to enhance operational efficiency and improve the consumer experience. As data gain 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. Over the predicted period, examples include chatbots, paperwork automation, and employee training gamification. Machine learning is expected to be used to automate financial processes.
• Post-COVID-19 pandemic, financial institutions are showing a growing interest in reaching and assisting customers through digital channels. Various digital solutions, including chatbots, support for account opening and management, and technical assistance, have seen a surge in adoption within the financial sector. Notably, fintech companies like Posh.Tech, Spixii, and numerous others now provide intelligent chatbots designed to facilitate essential customer-facing functions for banks
• Banks must enhance 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 various 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.

North America to Hold Significant Market Share

• The United States is expected to hold a substantial share in the market owing to the strong 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, which has driven the development of automated machine learning (AutoML).
• Various governments, including state and local governments, handle enormous quantities of citizen data, which had earlier been 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 federatedML is further expected to drive demand for AutoML.
• According to the Government of Canada, artificial intelligence (AI) technologies hold great promise for enhancing how the Canadian government serves its citizens. As the government investigates the use of artificial intelligence in government programs and services, it ensures that clear values, ethics, and rules guide it.
• While the US is trying to establish AutoML supremacy, Canada is also gearing up for such developments. For instance, in April 2023, ePayPolicy launched Payables Connect, the new addition to its suite of insurance payment and reconciliation products. It leverages ePay's existing integration and machine learning technology to completely automate the reconciliation, creation, and payment of due payables.
• 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 financial sector and the emerging educational interest among students, are expected to drive market growth.
• The region's AutoML marketplace is changing due to the cloud, and serverless computing allows creators to get ML applications up and running quickly.

Automated Machine Learning (AutoML) Industry Overview

The global automated machine learning market exhibits moderate fragmentation, with numerous players meeting market demands. Intensifying 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 industry leaders include Datarobot Inc., Amazon Web Services Inc., dotData Inc., IBM Corporation, and Dataiku.

In August 2023, DataRobot introduced a new generative artificial intelligence (AI) offering comprising platform capabilities and applied AI services designed to expedite the journey from concept to value with generative AI.

In August 2023, dotData Inc. launched dotData Ops, a next-generation no-code MLOps platform. This platform empowers ML engineers by delivering an intuitive, self-service environment for the efficient deployment and operationalization of data, feature, and prediction pipelines.

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 Assessment of the Impact of COVID-19 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 Users
  • 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