Dublin, April 06, 2020 (GLOBE NEWSWIRE) -- The "Automated Machine Learning Market Research Report: By Offering, Deployment Type, Enterprise Size, Application, Industry - Industry Size, Share, Development and Demand Forecast to 2030" report has been added to ResearchAndMarkets.com's offering.
The global automated machine learning (AutoML) market generated the revenue of $269.6 million in 2019, and is expected to reach $14,511.9 million by 2030, advancing at a CAGR of 43.7% during the forecast period (2020-2030).
The cloud category under the deployment type segment is expected to record the fastest growth during the forecast period. This can be ascribed to the enhanced scalability and flexibility offered by cloud-based platform, where clients can customize solutions and services as per their requirements.
With the increasing popularity of online shopping, demand for personalized content is increasing, owing to customers preference for products to meet their specific demand. Personalized product recommendations help companies to increase average order value. Thus, to meet the evolving needs of customers for updated products, companies are investing heavily in new technologies to offer best product recommendations. AutoML solutions find patterns in customer behavior from clickstream data, prior purchases, demographics, browsing history, and previous product searches, and create 1:1 personalized product recommendation list that matches consumer needs and preferences.
The rising importance of effective product assortment in retail store network is expected to generate immense opportunities in the automated machine learning market. Choosing the right mix of products in a retail store is very important for retailers in order to meet the needs of customers and retain customer base. AutoML solutions can be ideal for retailers for effective product assortment. The solution can look at various factors, such as location, customer segments, weather patterns, store display space, and past sales records, to find out which products would be the best fit for a given store location.
Together, North America and Europe are expected to hold over 65% share cumulatively in the automated machine learning market in 2030. All the major investments are being recorded in the U.S., Canada, Germany, U.K., and France. Further, technological advancement, developed IT infrastructure, and increasing adoption of emerging technologies are some of the key factors driving the growth of the market in the regions.
APAC is expected to register fastest growth in the market during the forecast period. This can be attributed to the rising economic growth, increasing investment in IT infrastructure, significant adoption of emerging technologies, and increasing government initiatives toward the development of artificial intelligence (AI) technology.
Key Topics Covered:
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
1.3.8.1 Value
1.4 Key Stakeholders
Chapter 2. Research Methodology
2.1 Secondary Research
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 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 Value Chain Analysis
4.3 Market Dynamics
4.3.1 Trends
4.3.1.1 Increasing preference for cloud-based AutoML platform
4.3.2 Drivers
4.3.2.1 Increasing demand for efficient fraud detection solution
4.3.2.2 Growing need for personalized product recommendation
4.3.2.3 Rising importance of predictive lead scoring
4.3.2.4 Impact analysis of drivers on market forecast
4.3.3 Restraints
4.3.3.1 Slow adoption in developing countries
4.3.3.2 Impact analysis of restraints on market forecast
4.3.4 Opportunities
4.3.4.1 Growing healthcare industry
4.3.4.2 Rising importance of effective product assortment
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
Chapter 5. Global Market Size and Forecast
5.1 By Offering
5.1.1 By Service
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
6.1.1 By Service
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
7.1.1 By Service
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
8.1.1 By Service
8.2 By Deployment Type
8.3 By Enterprise Size
8.4 By Application
8.5 By Industry
8.6 By Country
Chapter 9. MEA Market Size and Forecast
9.1 By Offering
9.1.1 By Service
9.2 By Deployment Type
9.3 By Enterprise Size
9.4 By Application
9.5 By Industry
9.6 By Country
Chapter 10. LATAM Market Size and Forecast
10.1 By Offering
10.1.1 By Service
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 Competitive Analysis of Key Players
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 Launches
Chapter 12. Company Profiles
12.1 DataRobot Inc.
12.2 H2O.ai Inc.
12.3 dotData Inc.
12.4 EdgeVerve Systems Limited
12.5 Amazon Web Services Inc.
12.6 Squark
12.7 Big Squid Inc.
12.8 SAS Institute Inc.
12.9 Microsoft Corporation
12.10 Determined AI
Chapter 13. Appendix
Companies Mentioned
- DataRobot Inc.
- H2O.ai Inc.
- dotData Inc.
- EdgeVerve Systems Limited
- Amazon Web Services Inc.
- Squark
- Big Squid Inc.
- SAS Institute Inc.
- Microsoft Corporation
- Determined AI
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