Product Code: 11843
In 2022, the automated machine learning market size stood at USD 631.0 million, which is projected to witness a 49.2% CAGR during 2022-2030, reaching USD 15,499.3 million by 2030 as per P&S Intelligence.
The major factors accountable for the development of the market include the growing demand for effective fraud-finding solutions and the rising need for personalized product recommendations.
In 2022, The platform category generated the larger revenue share, of 73%, on the basis of offering. This growth can be credited to the growing acceptance of such platforms across all verticals for operational cost reduction, customer service improvement, and fraud deduction.
Furthermore, the pandemic helped in lifting digital transformation in nearly every industry, like healthcare, manufacturing, and BFSI, which is contributing to the adoption of this technology.
In 2022, The cloud category held the higher market share, on the basis of deployment type. This is due to the advanced flexibility and scalability of cloud-based automated machine-learning channels, which clients can modify according to their needs. Furthermore, as the cloud lessens the infrastructure and operational expenses, a huge number of businesses are more and more accepting cloud-based solutions.
On the basis of application, the sales and marketing management category are set to witness the fastest growth in the coming years. this can be owed to the huge number of businesses are utilizing such platforms in order to gain insights into buyer emotion and offer content personalization, customer segmentation, customer engagement, and lead scoring.
On the basis of industry, the healthcare category is set to experience the highest growth in the coming years. This is due to the hike in the demand for ML by the healthcare sector for the early recognition of illnesses, training, research and treating patients fast and efficiently, while reducing money, time, and resources.
In 2022, North America accounted for the highest revenue in the automated machine learning market. this can be credited to the advanced information technology infrastructure, prosperous BFSI, IT & telecom, the existence of main AutoML platform providers, and the healthcare sector are the key factors boosting the market growth in the continent.
The APAC market is projected to experience the fastest growth in the forecast period. This is because of snowballing spending on the IT infrastructure, a growing number of government efforts for the growth of AI technologies, and smooth economic development.
Additionally, APAC nations are preferring destinations for IT outsourcing. Credited to this, IT businesses are obtaining significant requests for application development, which fuels the growth of the market.
Thus, the growing demand for effective fraud-finding solutions and the rising need for personalized product recommendations will drive the automated machine-learning industry in the future.
Table of Contents
Chapter 1. Research Background
- 1.1. Research Objectives
- 1.2. Market Definition
- 1.3. Research Scope
- 1.3.1. Market Segmentation by Offering
- 1.3.2. Market Segmentation by Deployment Type
- 1.3.3. Market Segmentation by Enterprise Size
- 1.3.4. Market Segmentation by Application
- 1.3.5. Market Segmentation by Industry
- 1.3.6. Market Segmentation by Region
- 1.3.7. Analysis Period
- 1.3.8. Market Data Reporting Unit
- 1.4. Key Stakeholders
Chapter 2. Research Methodology
- 2.1. Secondary Research
- 2.1.1. Paid
- 2.1.2. Unpaid
- 2.1.3. P&S Intelligence Database
- 2.2. Primary Research
- 2.2.1. Breakdown of Primary Research Respondents
- 2.2.1.1. By region
- 2.2.1.2. By industry participant
- 2.2.1.3. By company type
- 2.3. Market Size Estimation
- 2.4. Data Triangulation
- 2.5. Currency Conversion Rates
- 2.6. Assumptions for the Study
Chapter 3. Executive Summary
Chapter 4. Introduction
- 4.1. Definition of Market Segments
- 4.1.1. By Offering
- 4.1.1.1. Platform
- 4.1.1.2. Service
- 4.1.1.2.1. Professional
- 4.1.1.2.2. Managed
- 4.1.2. By Deployment Type
- 4.1.2.1. On-premises
- 4.1.2.2. Cloud
- 4.1.3. By Enterprise Size
- 4.1.3.1. Large enterprise
- 4.1.3.2. SME
- 4.1.4. By Application
- 4.1.4.1. Fraud detection
- 4.1.4.2. Sales & marketing management
- 4.1.4.3. Medical testing
- 4.1.4.4. Transport optimization
- 4.1.4.5. Others
- 4.1.5. By Industry
- 4.1.5.1. BFSI
- 4.1.5.2. IT & telecom
- 4.1.5.3. Healthcare
- 4.1.5.4. Government
- 4.1.5.5. Retail
- 4.1.5.6. Manufacturing
- 4.1.5.7. Others
- 4.2. Market Dynamics
- 4.2.1. Trends
- 4.2.1.1. Increasing preference for cloud-based platforms
- 4.2.2. Drivers
- 4.2.2.1. Increasing demand for efficient fraud detection solutions
- 4.2.2.2. Growing need for personalized product recommendations
- 4.2.2.3. Rising importance of predictive lead scoring
- 4.2.2.4. Impact analysis of drivers on market forecast
- 4.2.3. Restraints
- 4.2.3.1. Shortage of technologically skilled personnel
- 4.2.3.2. Impact analysis of restraints on market forecast
- 4.2.4. Opportunities
- 4.2.4.1. Growing healthcare industry
- 4.2.4.2. Rising importance of effective product assortment
- 4.3. Impact of COVID-19 on AutoML Market
- 4.4. Porter's Five Forces Analysis
- 4.4.1. Bargaining Power of Buyers
- 4.4.2. Bargaining Power of Suppliers
- 4.4.3. Threat of New Entrants
- 4.4.4. Intensity of Rivalry
- 4.4.5. Threat of Substitutes
- 4.5. Advantages of AutoML
- 4.6. Value Chain Analysis
Chapter 5. Global Market Size and Forecast
- 5.1. By Offering
- 5.2. By Deployment Type
- 5.3. By Enterprise Size
- 5.4. By Application
- 5.5. By Industry
- 5.6. By Region
Chapter 6. North America Market Size and Forecast
- 6.1. By Offering
- 6.2. By Deployment Type
- 6.3. By Enterprise Size
- 6.4. By Application
- 6.5. By Industry
- 6.6. By Country
Chapter 7. Europe Market Size and Forecast
- 7.1. By Offering
- 7.2. By Deployment Type
- 7.3. By Enterprise Size
- 7.4. By Application
- 7.5. By Industry
- 7.6. By Country
Chapter 8. APAC Market Size and Forecast
- 8.1. By Offering
- 8.2. By Deployment Type
- 8.3. By Enterprise Size
- 8.4. By Application
- 8.5. By Industry
- 8.6. By Country
Chapter 9. LATAM Market Size and Forecast
- 9.1. By Offering
- 9.2. By Deployment Type
- 9.3. By Enterprise Size
- 9.4. By Application
- 9.5. By Industry
- 9.6. By Country
Chapter 10. MEA Market Size and Forecast
- 10.1. By Offering
- 10.2. By Deployment Type
- 10.3. By Enterprise Size
- 10.4. By Application
- 10.5. By Industry
- 10.6. By Country
Chapter 11. Competitive Landscape
- 11.1. List of Key Players
- 11.2. Market Presence Metric (Based on Popularity & Web Traffic)
- 11.3. Recent Activities of Major Players
- 11.4. Strategic Developments of Key Players
- 11.4.1. Mergers and Acquisitions
- 11.4.2. Partnerships
- 11.4.3. Product/Service Launches
- 11.4.4. Others
Chapter 12. Company Profiles
- 12.1. DataRobot Inc.
- 12.1.1. Business Overview
- 12.1.2. Product and Service Offerings
- 12.2. H2O.ai Inc.
- 12.2.1. Business Overview
- 12.2.2. Product and Service Offerings
- 12.3. dotData Inc.
- 12.3.1. Business Overview
- 12.3.2. Product and Service Offerings
- 12.4. EdgeVerve Systems Limited
- 12.4.1. Business Overview
- 12.4.2. Product and Service Offerings
- 12.5. Amazon Web Services Inc.
- 12.5.1. Business Overview
- 12.5.2. Product and Service Offerings
- 12.6. Squark
- 12.6.1. Business Overview
- 12.6.2. Product and Service Offerings
- 12.7. Qlik Technologies Inc.
- 12.7.1. Business Overview
- 12.7.2. Product and Service Offerings
- 12.8. SAS Institute Inc.
- 12.8.1. Business Overview
- 12.8.2. Product and Service Offerings
- 12.9. Microsoft Corporation
- 12.9.1. Business Overview
- 12.9.2. Product and Service offerings
- 12.9.3. Key Financial Summary
- 12.1. Determined.ai Inc.
- 12.10.1. Business Overview
- 12.10.2. Product and Service Offerings
- 12.11. JADBio - Gnosis DA S.A.
- 12.11.1. Business Overview
- 12.11.2. Product and Service Offerings
- 12.12. BigML Inc.
- 12.12.1. Business Overview
- 12.12.2. Product and Service Offerings
- 12.13. Splunk Inc.
- 12.13.1. Business Overview
- 12.13.2. Product and Service Offerings
- 12.13.3. Key Financial Summary
- 12.14. Tellius Inc.
- 12.14.1. Business Overview
- 12.14.2. Product and Service Offerings
- 12.15. Aible Inc.
- 12.15.1. Business Overview
- 12.15.2. Product and Service Offerings
- 12.16. International Business Machines (IBM) Corporation
- 12.16.1. Business Overview
- 12.16.2. Product and Service Offerings
- 12.16.3. Key Financial Summary
- 12.17. Google LLC
- 12.17.1. Business Overview
- 12.17.2. Product and Service Offerings
- 12.18. RapidMiner
- 12.18.1. Business Overview
- 12.18.2. Product and Service Offerings
- 12.19. TAZI AI
- 12.19.1. Business Overview
- 12.19.2. Product and Service Offerings
- 12.2. MLJAR Sp. z o. o.
- 12.20.1. Business Overview
- 12.20.2. Product and Service Offerings
- 12.21. Akkio Inc.
- 12.21.1. Business Overview
- 12.21.2. Product and Service Offerings
- 12.22. Enhencer LLC
- 12.22.1. Business Overview
- 12.22.2. Product and Service Offerings
- 12.23. Dataiku
- 12.23.1. Business Overview
- 12.23.2. Product and Service Offerings
Chapter 13. Appendix
- 13.1. Sources and References
- 13.2. Related Reports