Automated Machine Learning Market Size, Share | Report 2022-2030
The global AutoML market size will grow on the backdrop of 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 to boost customer trust and ensure compliance with laws.
Automated machine learning (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.
Bullish demand for AutoML is mainly attributed to its ability to help enterprises boost insights and enhance model accuracy by minimizing chances for error or bias. End-users, including BFSI, healthcare, IT & telecom and retail, are expected to inject funds into AutoML to rev up their AI efforts to create a valuable pipeline to automate data preprocessing, model selection and pre-trained models. Prominently, the healthcare sector exhibited increased traction for machine-learning-powered chatbots to leverage contactless screening and boost the patient experience. The use of Automated machine learning has become instrumental in boosting service quality, enhancing bed occupancy estimation, boosting patient billing estimation and minimizing costs.
Stakeholders anticipate the AutoML services to gain ground on the back of soaring demand for maintenance and consulting services. Organizations and enterprises are likely to further their investments in services to streamline workflows and boost productivity. The application of Automated machine learning will further grow due to an increased chance of reduced errors and human bias.
In terms of deployment type, the cloud-based segment will exhibit notable growth due to the trend for custom ML models and the demand for scalability. Cloud AutoML has become trendier across businesses for image recognition and training and managing models. Furthermore, some factors, such as faster turnaround time for the production-ready models, increased accuracy, and simple graphical user interface has encouraged organizations to invest in cloud Automated machine learning .
Based on application, the fraud detection segment will account for a considerable share of the AutoML market share. 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.
With respect to vertical, 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. AutoML 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.
North America AutoML market share will observe a prominent growth in the wake of rising investments from the BFSI, retail, and healthcare sectors. The U.S. and Canada will witness bullish investments in AI technology and machine learning to automate workflow and enable firms’ data scientists to prioritize more complex issues. While the media, Information and Communication Technology (ICT) sector and professional services will augment investments in machine learning and AI, manufacturing, mining, and utilities could be in the nascent stage of adoption of the technology across the region. The next few years are expected to provide compelling growth opportunities for AutoML companies as end-users seek a new wave of opportunities in automation technologies to take the lead in the industry.
The competitive scenario alludes to an increased focus on innovation, technological advancements, collaboration, product rollouts and mergers & acquisitions. To illustrate, in March 2022, Oracle announced the addition of AutoML to its MySQL HeatWave service. In June 2022, Google expanded its managed AI service Vertex, featuring Tabular Workflows to boost customizability.
Market Segmentation
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Distribution Channel Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on Automated Machine Learning Market
5.1. COVID-19 Landscape: Automated Machine Learning Industry Impact
5.2. COVID 19 - Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global Automated Machine Learning Market, By Offering
8.1. Automated Machine Learning Market, by Offering, 2022-2030
8.1.1. Platform
8.1.1.1. Market Revenue and Forecast (2017-2030)
8.1.2. Service
8.1.2.1. Market Revenue and Forecast (2017-2030)
Chapter 9. Global Automated Machine Learning Market, By Deployment
9.1. Automated Machine Learning Market, by Deployment, 2022-2030
9.1.1. On-Premises
9.1.1.1. Market Revenue and Forecast (2017-2030)
9.1.2. Cloud
9.1.2.1. Market Revenue and Forecast (2017-2030)
Chapter 10. Global Automated Machine Learning Market, By Application
10.1. Automated Machine Learning Market, by Application, 2022-2030
10.1.1. Fraud Detection
10.1.1.1. Market Revenue and Forecast (2017-2030)
10.1.2. Sales & Marketing Management
10.1.2.1. Market Revenue and Forecast (2017-2030)
10.1.3. Medical Testing
10.1.3.1. Market Revenue and Forecast (2017-2030)
10.1.4. Transport Optimization
10.1.4.1. Market Revenue and Forecast (2017-2030)
Chapter 11. Global Automated Machine Learning Market, By Vertical
11.1. Automated Machine Learning Market, by Vertical, 2022-2030
11.1.1. BFSI
11.1.1.1. Market Revenue and Forecast (2017-2030)
11.1.2. IT & Telecom
11.1.2.1. Market Revenue and Forecast (2017-2030)
11.1.3. Healthcare
11.1.3.1. Market Revenue and Forecast (2017-2030)
11.1.4. Government
11.1.4.1. Market Revenue and Forecast (2017-2030)
11.1.5. Retail
11.1.5.1. Market Revenue and Forecast (2017-2030)
11.1.6. Manufacturing
11.1.6.1. Market Revenue and Forecast (2017-2030)
11.1.7. Others
11.1.7.1. Market Revenue and Forecast (2017-2030)
Chapter 12. Global Automated Machine Learning Market, Regional Estimates and Trend Forecast
12.1. North America
12.1.1. Market Revenue and Forecast, by Offering (2017-2030)
12.1.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.1.3. Market Revenue and Forecast, by Application (2017-2030)
12.1.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.1.5. U.S.
12.1.5.1. Market Revenue and Forecast, by Offering (2017-2030)
12.1.5.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.1.5.3. Market Revenue and Forecast, by Application (2017-2030)
12.1.5.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.1.6. Rest of North America
12.1.6.1. Market Revenue and Forecast, by Offering (2017-2030)
12.1.6.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.1.6.3. Market Revenue and Forecast, by Application (2017-2030)
12.1.6.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.2. Europe
12.2.1. Market Revenue and Forecast, by Offering (2017-2030)
12.2.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.2.3. Market Revenue and Forecast, by Application (2017-2030)
12.2.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.2.5. UK
12.2.5.1. Market Revenue and Forecast, by Offering (2017-2030)
12.2.5.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.2.5.3. Market Revenue and Forecast, by Application (2017-2030)
12.2.5.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.2.6. Germany
12.2.6.1. Market Revenue and Forecast, by Offering (2017-2030)
12.2.6.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.2.6.3. Market Revenue and Forecast, by Application (2017-2030)
12.2.6.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.2.7. France
12.2.7.1. Market Revenue and Forecast, by Offering (2017-2030)
12.2.7.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.2.7.3. Market Revenue and Forecast, by Application (2017-2030)
12.2.7.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.2.8. Rest of Europe
12.2.8.1. Market Revenue and Forecast, by Offering (2017-2030)
12.2.8.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.2.8.3. Market Revenue and Forecast, by Application (2017-2030)
12.2.8.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.3. APAC
12.3.1. Market Revenue and Forecast, by Offering (2017-2030)
12.3.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.3.3. Market Revenue and Forecast, by Application (2017-2030)
12.3.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.3.5. India
12.3.5.1. Market Revenue and Forecast, by Offering (2017-2030)
12.3.5.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.3.5.3. Market Revenue and Forecast, by Application (2017-2030)
12.3.5.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.3.6. China
12.3.6.1. Market Revenue and Forecast, by Offering (2017-2030)
12.3.6.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.3.6.3. Market Revenue and Forecast, by Application (2017-2030)
12.3.6.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.3.7. Japan
12.3.7.1. Market Revenue and Forecast, by Offering (2017-2030)
12.3.7.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.3.7.3. Market Revenue and Forecast, by Application (2017-2030)
12.3.7.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.3.8. Rest of APAC
12.3.8.1. Market Revenue and Forecast, by Offering (2017-2030)
12.3.8.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.3.8.3. Market Revenue and Forecast, by Application (2017-2030)
12.3.8.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.4. MEA
12.4.1. Market Revenue and Forecast, by Offering (2017-2030)
12.4.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.4.3. Market Revenue and Forecast, by Application (2017-2030)
12.4.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.4.5. GCC
12.4.5.1. Market Revenue and Forecast, by Offering (2017-2030)
12.4.5.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.4.5.3. Market Revenue and Forecast, by Application (2017-2030)
12.4.5.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.4.6. North Africa
12.4.6.1. Market Revenue and Forecast, by Offering (2017-2030)
12.4.6.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.4.6.3. Market Revenue and Forecast, by Application (2017-2030)
12.4.6.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.4.7. South Africa
12.4.7.1. Market Revenue and Forecast, by Offering (2017-2030)
12.4.7.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.4.7.3. Market Revenue and Forecast, by Application (2017-2030)
12.4.7.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.4.8. Rest of MEA
12.4.8.1. Market Revenue and Forecast, by Offering (2017-2030)
12.4.8.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.4.8.3. Market Revenue and Forecast, by Application (2017-2030)
12.4.8.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.5. Latin America
12.5.1. Market Revenue and Forecast, by Offering (2017-2030)
12.5.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.5.3. Market Revenue and Forecast, by Application (2017-2030)
12.5.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.5.5. Brazil
12.5.5.1. Market Revenue and Forecast, by Offering (2017-2030)
12.5.5.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.5.5.3. Market Revenue and Forecast, by Application (2017-2030)
12.5.5.4. Market Revenue and Forecast, by Vertical (2017-2030)
12.5.6. Rest of LATAM
12.5.6.1. Market Revenue and Forecast, by Offering (2017-2030)
12.5.6.2. Market Revenue and Forecast, by Deployment (2017-2030)
12.5.6.3. Market Revenue and Forecast, by Application (2017-2030)
12.5.6.4. Market Revenue and Forecast, by Vertical (2017-2030)
Chapter 13. Research Methodology
13.1. Primary Research
13.2. Secondary Research
13.3. Assumptions
Chapter 14. Appendix
14.1. About Us
14.2. Glossary of Terms
Cross-segment Market Size and Analysis for
Mentioned Segments
Additional Company Profiles (Upto 5 With No Cost)
Additional Countries (Apart From Mentioned Countries)
Country/Region-specific Report
Go To Market Strategy
Region Specific Market DynamicsRegion Level Market Share Import Export AnalysisProduction AnalysisOthers