세계의 자동 머신러닝 시장 규모 조사 및 예측 : 솔루션별, 자동화 유형별, 최종 사용자별, 지역별 분석(2023-2030년)

Global Automated Machine Learning Market is valued at approximately USD 0.87 billion in 2022 and is anticipated to grow with a healthy growth rate of more than 43.90% during the forecast period 2023-2030. Automated Machine Learning refers to the process of automating the end-to-end process of applying machine learning to real-world problems. The purpose of automated machine learning is to make machine learning more accessible to non-experts and streamline the workflow for experienced practitioners. It involves automating various steps in the machine learning pipeline, including data preprocessing, feature engineering, model selection, hyperparameter tuning, and model deployment. The Automated Machine Learning Market is expanding because of factors such as the growing volume of data and rising demand for machine-learning-powered chatbots. As a result, the demand for Automated Machine Learning has progressively increased in the international market during the forecast period 2023-2030.

Large datasets frequently include more complicated connections and patterns, and automated machine learning is getting access to large amounts of data, and can handle and record complex data structures, resulting in more sophisticated and accurate models. According to Statista, in 2020, the global volume of data accounts for 64.2 zettabytes and is projected to reach up to 181 zettabytes by the year 2025. Another important factor that drives the Automated Machine Learning Market is the increasing demand for the machine-learning-powered chatbot. Machine-learning-powered chatbots often require the deployment of models that can understand and generate human-like responses. Automated Machine Learning tools facilitate rapid model development and deployment, allowing organizations to quickly implement chatbot solutions without the need for extensive manual model tuning. In addition, as per Statista, the global chatbot market is projected to reach up to USD 1.25 billion by the year 2025. Moreover, the rising trend of cloud-based machine learning models and government initiatives towards the adoption of fraud detection technology is anticipated to create a lucrative growth opportunity for the market over the forecast period. However, the lack of standardization and rising threat to data privacy is going to impede overall market growth throughout the forecast period of 2023-2030.

The key regions considered for the Global Automated Machine Learning Market study includes Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market in 2022 owing to the increasing demand for artificial intelligence solutions in the region. The growing demand for quick turnaround times in deploying AI applications is addressed by AutoML. Automated processes speed up model development, allowing organizations to bring AI solutions to the market faster. This agility is crucial in industries where timely implementation of AI can provide a competitive advantage. The region's dominant performance is anticipated to propel the overall demand for Automated Machine Learning. Furthermore, Asia Pacific is expected to grow fastest over the forecast period, owing to factors such as supportive government initiatives towards the expansion of artificial intelligence in the region.

Major market player included in this report are:

• DataRobot, Inc
• Amazon Web Services, Inc
• DotData, Inc
• Internatinal Business Machine Corporation
• Dataiku Inc
• SAS Institute Inc
• Microsoft Corporation
• Google LLC
• H2O.ai Inc
• Aible Inc

Recent Developments in the Market:

• In September 2023, Fujitsu Limited and the Linux Foundation launched automated machine learning and artificial intelligence technologies. The two projects are going to provide customers with software that automatically develops code for new machine learning models, as well as technology that eliminates latent biases in training data. The Linux Foundation approved the incubation of two new projects, "SapientML" and "Intersectional Fairness," to encourage developers around the world to continue experimenting and innovating with AI and machine learning technologies, with plans to host future activities such as hackathons to engage and build a community to promote open-source AI. These projects are going to further democratise AI, resulting in a world in which developers around the world can easily and securely use cutting-edge technologies on open platforms to create new applications and find innovative solutions to business and societal challenges.

Global Automated Machine Learning Market Report Scope:

• Historical Data - 2020 - 2021
• Base Year for Estimation - 2022
• Forecast period - 2023-2030
• Report Coverage - Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
• Segments Covered - Solution, Automation Type, End Users, Region
• Regional Scope - North America; Europe; Asia Pacific; Latin America; Middle East & Africa
• Customization Scope - Free report customization (equivalent up to 8 analyst's working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Solution

• Standalone or On-Premise
• Cloud

By Automation Type

• Data Processing
• Feature Engineering
• Modeling
• Visualization

By End Users

• BFSI
• Retail and E-Commerce
• Healthcare
• Manufacturing
• Other End Users

By Region:

• North America
• U.S.
• Canada
• Europe
• UK
• Germany
• France
• Spain
• Italy
• ROE
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• RoAPAC
• Latin America
• Brazil
• Mexico
• Middle East & Africa
• Saudi Arabia
• South Africa
• Rest of Middle East & Africa

Table of Contents

Chapter 1.Executive Summary

• 1.1.Market Snapshot
• 1.2.Global & Segmental Market Estimates & Forecasts, 2020-2030 (USD Billion)
  • 1.2.1.Automated Machine Learning Market, by Region, 2020-2030 (USD Billion)
  • 1.2.2.Automated Machine Learning Market, by Solution, 2020-2030 (USD Billion)
  • 1.2.3.Automated Machine Learning Market, by Automation Type, 2020-2030 (USD Billion)
  • 1.2.4.Automated Machine Learning Market, by End Users, 2020-2030 (USD Billion)
• 1.3.Key Trends
• 1.4.Estimation Methodology
• 1.5.Research Assumption

Chapter 2.Global Automated Machine Learning Market Definition and Scope

• 2.1.Objective of the Study
• 2.2.Market Definition & Scope
  • 2.2.1.Industry Evolution
  • 2.2.2.Scope of the Study
• 2.3.Years Considered for the Study
• 2.4.Currency Conversion Rates

Chapter 3.Global Automated Machine Learning Market Dynamics

• 3.1.Automated Machine Learning Market Impact Analysis (2020-2030)
  • 3.1.1.Market Drivers
    • 3.1.1.1.Growing volume of data
    • 3.1.1.2.Rising demand for machine-learning-powered chatbot
  • 3.1.2.Market Challenges
    • 3.1.2.1.Lack of standardization
    • 3.1.2.2.Rising threat to data privacy
  • 3.1.3.Market Opportunities
    • 3.1.3.1.Rising trend of cloud-based machine learning models
    • 3.1.3.2.Government initiatives towards adoption of fraud detection technology

Chapter 4.Global Automated Machine Learning Market Industry Analysis

• 4.1.Porter's 5 Force Model
  • 4.1.1.Bargaining Power of Suppliers
  • 4.1.2.Bargaining Power of Buyers
  • 4.1.3.Threat of New Entrants
  • 4.1.4.Threat of Substitutes
  • 4.1.5.Competitive Rivalry
• 4.2.Porter's 5 Force Impact Analysis
• 4.3.PEST Analysis
  • 4.3.1.Political
  • 4.3.2.Economical
  • 4.3.3.Social
  • 4.3.4.Technological
  • 4.3.5.Environmental
  • 4.3.6.Legal
• 4.4.Top investment opportunity
• 4.5.Top winning strategies
• 4.6.COVID-19 Impact Analysis
• 4.7.Disruptive Trends
• 4.8.Industry Expert Perspective
• 4.9.Analyst Recommendation & Conclusion

Chapter 5.Global Automated Machine Learning Market, by Solution

• 5.1.Market Snapshot
• 5.2.Global Automated Machine Learning Market by Solution, Performance - Potential Analysis
• 5.3.Global Automated Machine Learning Market Estimates & Forecasts by Solution 2020-2030 (USD Billion)
• 5.4.Automated Machine Learning Market, Sub Segment Analysis
  • 5.4.1.Standalone or On-Premise
  • 5.4.2.Cloud

Chapter 6.Global Automated Machine Learning Market, by Automation Type

• 6.1.Market Snapshot
• 6.2.Global Automated Machine Learning Market by Automation Type, Performance - Potential Analysis
• 6.3.Global Automated Machine Learning Market Estimates & Forecasts by Automation Type 2020-2030 (USD Billion)
• 6.4.Automated Machine Learning Market, Sub Segment Analysis
  • 6.4.1.Data Processing
  • 6.4.2.Feature Engineering
  • 6.4.3.Modeling
  • 6.4.4.Visualization

Chapter 7.Global Automated Machine Learning Market, by End Users

• 7.1.Market Snapshot
• 7.2.Global Automated Machine Learning Market by End Users, Performance - Potential Analysis
• 7.3.Global Automated Machine Learning Market Estimates & Forecasts by End Users 2020-2030 (USD Billion)
• 7.4.Automated Machine Learning Market, Sub Segment Analysis
  • 7.4.1.BFSI
  • 7.4.2.Retail and E-Commerce
  • 7.4.3.Healthcare
  • 7.4.4.Manufacturing
  • 7.4.5.Other End Users

Chapter 8.Global Automated Machine Learning Market, Regional Analysis

• 8.1.Top Leading Countries
• 8.2.Top Emerging Countries
• 8.3.Automated Machine Learning Market, Regional Market Snapshot
• 8.4.North America Automated Machine Learning Market
  • 8.4.1.U.S. Automated Machine Learning Market
    • 8.4.1.1.Solution breakdown estimates & forecasts, 2020-2030
    • 8.4.1.2.Automation Type breakdown estimates & forecasts, 2020-2030
    • 8.4.1.3.End Users breakdown estimates & forecasts, 2020-2030
  • 8.4.2.Canada Automated Machine Learning Market
• 8.5.Europe Automated Machine Learning Market Snapshot
  • 8.5.1.U.K. Automated Machine Learning Market
  • 8.5.2.Germany Automated Machine Learning Market
  • 8.5.3.France Automated Machine Learning Market
  • 8.5.4.Spain Automated Machine Learning Market
  • 8.5.5.Italy Automated Machine Learning Market
  • 8.5.6.Rest of Europe Automated Machine Learning Market
• 8.6.Asia-Pacific Automated Machine Learning Market Snapshot
  • 8.6.1.China Automated Machine Learning Market
  • 8.6.2.India Automated Machine Learning Market
  • 8.6.3.Japan Automated Machine Learning Market
  • 8.6.4.Australia Automated Machine Learning Market
  • 8.6.5.South Korea Automated Machine Learning Market
  • 8.6.6.Rest of Asia Pacific Automated Machine Learning Market
• 8.7.Latin America Automated Machine Learning Market Snapshot
  • 8.7.1.Brazil Automated Machine Learning Market
  • 8.7.2.Mexico Automated Machine Learning Market
• 8.8.Middle East & Africa Automated Machine Learning Market
  • 8.8.1.Saudi Arabia Automated Machine Learning Market
  • 8.8.2.South Africa Automated Machine Learning Market
  • 8.8.3.Rest of Middle East & Africa Automated Machine Learning Market

Chapter 9.Competitive Intelligence

• 9.1.Key Company SWOT Analysis
• 9.2.Top Market Strategies
• 9.3.Company Profiles
  • 9.3.1. DataRobot, Inc
    • 9.3.1.1.Key Information
    • 9.3.1.2.Overview
    • 9.3.1.3.Financial (Subject to Data Availability)
    • 9.3.1.4.Product Summary
    • 9.3.1.5.Recent Developments
  • 9.3.2.Amazon Web Services, Inc
  • 9.3.3.DotData, Inc
  • 9.3.4.Internatinal Business Machine Corporation
  • 9.3.5.Dataiku Inc
  • 9.3.6.SAS Institute Inc
  • 9.3.7.Microsoft Corporation
  • 9.3.8.Google LLC
  • 9.3.9.H2O.ai Inc
  • 9.3.10.Aible Inc

Chapter 10.Research Process

• 10.1.Research Process
  • 10.1.1.Data Mining
  • 10.1.2.Analysis
  • 10.1.3.Market Estimation
  • 10.1.4.Validation
  • 10.1.5.Publishing
• 10.2.Research Attributes
• 10.3.Research Assumption