Automated Machine Learning Market - Global Size, Share, Trend Analysis, Opportunity and Forecast Report, 2019-2029, Segmented By Solution ; By Automation Type ; By End User ; By Region

Description

Global Automated Machine Learning (AutoML) Market Size Booming at Robust CAGR of 44.56% to Reach USD 8.76 Billion by 2029

Global Automated Machine Learning (AutoML) Market is flourishing because of the spurring demand for efficient fraud detection solutions and for enhanced ML expertise.

BlueWeave Consulting, a leading strategic consulting and market research firm, in its recent study, estimated the Global Automated Machine Learning (AutoML) Market size at USD 0.96 billion in 2022. During the forecast period between 2023 and 2029, BlueWeave expects Global Automated Machine Learning (AutoML) Market size to expand at a robust CAGR of 44.56% reaching a value of USD 8.76 billion by 2029. Major growth drivers for the Global Automated Machine Learning (AutoML) Market include an increasing demand for advanced fraud detection solutions is driving the growth of the global AutoML market. Data analysis techniques, particularly supervised neural networks, are highly valued for their effectiveness in fraud detection through methods such as forecasting, clustering, and classification. Organizations are anticipated to invest in AutoML to enhance customer trust and ensure compliance with regulations. Notably, the adoption of AutoML is gaining momentum, as it reduces the number of knowledge workers required for implementing and training ML models. Also, the strong demand for AutoML is primarily driven by its capacity to assist enterprises in improving insights and enhancing model accuracy while minimizing the potential for errors or biases. Major sectors, including BFSI, healthcare, IT & telecom, and retail, are expected to allocate resources to AutoML to accelerate their AI adoption. It involves creating a robust pipeline for automating data preprocessing, model selection, and the utilization of pre-trained models. Notably, the healthcare sector has shown increased interest in ML-powered chatbots for contactless screening, thereby enhancing the overall patient experience. As a result, such aspects are expected to boost the expansion of the Global Automated Machine Learning (AutoML) Market during the forecast period. However, limited awareness about AutoML is anticipated to restrain the overall market growth during the period in analysis.

Impact of COVID-19 on Global Automated Machine Learning (AutoML) Market

COVID-19 pandemic had a mixed impact on the Global Automated Machine Learning (AutoML) Market. On one hand, the crisis spurred positive developments, such as hastening digital transformation efforts and fostering a heightened demand for AI and ML solutions across various sectors. Businesses, in response to the pandemic, sought to automate forecasting and decision-making processes, leading to increased utilization of AutoML systems. Conversely, the pandemic brought adverse effects, disrupting supply chains and compelling businesses to implement cost-cutting measures. It resulted in reduced IT budgets and a deceleration in the adoption of emerging innovative technologies, including AutoML. Furthermore, the pandemic underscored the imperative for ethical and transparent AI solutions, causing a slowdown in the adoption of AutoML platforms that lacked interpretability and transparency. The recognition of the importance of moral and open AI solutions became a hindrance to the swift adoption of certain AutoML platforms.

Global Automated Machine Learning (AutoML) Market - By End User

Based on end user, the Global Automated Machine Learning (AutoML) Market is divided into BFSI, Retail & E-Commerce, Healthcare, and Manufacturing segments. The BFSI segment is expected to hold the highest share in the Global Automated Machine Learning (AutoML) Market by end user during the forecast period. The BFSI sector is increasingly leveraging AI and ML to enhance operational efficiency and improve customer experiences. The growing emphasis on data has led to an increased demand for ML applications in BFSI. AutoML utilizes voluminous data, cost-effective processing capacity, and affordable storage to deliver precise and swift results. Collaborating with fintech services enables businesses to adapt to modern requirements and regulations, ensuring enhanced safety and security. Intelligent process automation, powered by ML, enables finance companies to automate repetitive tasks, leading to increased productivity. The BFSI sector is at the forefront of the AutoML market, primarily due to its adoption of AI and ML solutions for fraud detection, risk management, and customer service.

Global Automated Machine Learning (AutoML) Market - By Region

The in-depth research report on the Global Automated Machine Learning (AutoML) Market covers various country-specific markets across five major regions: North America, Europe, Asia Pacific, Latin America, and Middle East and Africa. Asia Pacific region dominates the Global Automated Machine Learning (AutoML) Market. It is fueled by the escalating IT spending and widespread FinTech adoption. Governments in Asia Pacific countries are actively integrating AI across various sectors, fostering the expansion of local markets. In China, a notable surge in ML adoption is observed, with businesses utilizing the technology for financial fraud detection, product recommendations, and industrial process optimization. The success of ML initiatives relies on robust infrastructure and reliable data. The AI market in Japan is expected to thrive, driven by the global demand for AI in robotics, speech recognition, and visual recognition. South Korea's substantial investments in advanced technologies, including AI and ML, are expected to contribute to the growth of Asia Pacific AutoML Market.

Competitive Landscape

Major players operating in the Global Automated Machine Learning (AutoML) Market include DataRobot Inc., Amazon Web Services Inc., dotData Inc., IBM Corporation, Dataiku, SAS Institute Inc., Microsoft Corporation, Google LLC (Alphabet Inc.), H2O.ai, and Aible Inc. To further enhance their market share, these companies employ various strategies, including mergers and acquisitions, partnerships, joint ventures, license agreements, and new product launches.

The in-depth analysis of the report provides information about growth potential, upcoming trends, and statistics of Global Automated Machine Learning (AutoML) Market. It also highlights the factors driving forecasts of total market size. The report promises to provide recent technology trends in Global Automated Machine Learning (AutoML) Market and industry insights to help decision-makers make sound strategic decisions. Furthermore, the report also analyzes the growth drivers, challenges, and competitive dynamics of the market.

Product Code: BWC231049

1. Research Framework

1.1. Research Objective
1.2. Product Overview
1.3. Market Segmentation

2. Executive Summary

3. Global Automated Machine Learning (AutoML) Market Insights

3.1. Industry Value Chain Analysis
3.2. DROC Analysis
- 3.2.1. Growth Drivers
  - 3.2.1.1. Increasing demand for efficient fraud detection solutions
  - 3.2.1.2. Growing demand for machine learning expertise
- 3.2.2. Restraints
  - 3.2.2.1. Limited awareness
- 3.2.3. Opportunities
  - 3.2.3.1. Advancement in technology
- 3.2.4. Challenges
  - 3.2.4.1. Data quality issues
3.3. Technological Advancements/Recent Developments
3.4. Regulatory Framework
3.5. Porter's Five Forces Analysis
- 3.5.1. Bargaining Power of Suppliers
- 3.5.2. Bargaining Power of Buyers
- 3.5.3. Threat of New Entrants
- 3.5.4. Threat of Substitutes
- 3.5.5. Intensity of Rivalry

4. Global Automated Machine Learning (AutoML) Market Overview

4.1. Market Size & Forecast, 2019-2029
- 4.1.1. By Value (USD Billion)
4.2. Market Share & Forecast
- 4.2.1. By Solution
  - 4.2.1.1. Standalone or On-Premise
  - 4.2.1.2. Cloud
- 4.2.2. By Automation Type
  - 4.2.2.1. Data Processing
  - 4.2.2.2. Feature Engineering
  - 4.2.2.3. Modeling
  - 4.2.2.4. Visualization
- 4.2.3. By End User
  - 4.2.3.1. BFSI
  - 4.2.3.2. Retail & E-Commerce
  - 4.2.3.3. Healthcare
  - 4.2.3.4. Manufacturing
  - 4.2.3.5. Others
- 4.2.4. By Region
  - 4.2.4.1. North America
  - 4.2.4.2. Europe
  - 4.2.4.3. Asia Pacific (APAC)
  - 4.2.4.4. Latin America (LATAM)
  - 4.2.4.5. Middle East and Africa (MEA)

5. North America Automated Machine Learning (AutoML) Market

5.1. Market Size & Forecast, 2019-2029
- 5.1.1. By Value (USD Billion)
5.2. Market Share & Forecast
- 5.2.1. By Solution
- 5.2.2. By Automation Type
- 5.2.3. By End User
- 5.2.4. By Country
  - 5.2.4.1. United States
  - 5.2.4.1.1. By Solution
  - 5.2.4.1.2. By Automation Type
  - 5.2.4.1.3. By End User
  - 5.2.4.2. Canada
  - 5.2.4.2.1. By Solution
  - 5.2.4.2.2. By Automation Type
  - 5.2.4.2.3. By End User

6. Europe Automated Machine Learning (AutoML) Market

6.1. Market Size & Forecast, 2019-2029
- 6.1.1. By Value (USD Billion)
6.2. Market Share & Forecast
- 6.2.1. By Solution
- 6.2.2. By Automation Type
- 6.2.3. By End User
- 6.2.4. By Country
  - 6.2.4.1. Germany
  - 6.2.4.1.1. By Solution
  - 6.2.4.1.2. By Automation Type
  - 6.2.4.1.3. By End User
  - 6.2.4.2. United Kingdom
  - 6.2.4.2.1. By Solution
  - 6.2.4.2.2. By Automation Type
  - 6.2.4.2.3. By End User
  - 6.2.4.3. Italy
  - 6.2.4.3.1. By Solution
  - 6.2.4.3.2. By Automation Type
  - 6.2.4.3.3. By End User
  - 6.2.4.4. France
  - 6.2.4.4.1. By Solution
  - 6.2.4.4.2. By Automation Type
  - 6.2.4.4.3. By End User
  - 6.2.4.5. Spain
  - 6.2.4.5.1. By Solution
  - 6.2.4.5.2. By Automation Type
  - 6.2.4.5.3. By End User
  - 6.2.4.6. Belgium
  - 6.2.4.6.1. By Solution
  - 6.2.4.6.2. By Automation Type
  - 6.2.4.6.3. By End User
  - 6.2.4.7. Russia
  - 6.2.4.7.1. By Solution
  - 6.2.4.7.2. By Automation Type
  - 6.2.4.7.3. By End User
  - 6.2.4.8. The Netherlands
  - 6.2.4.8.1. By Solution
  - 6.2.4.8.2. By Automation Type
  - 6.2.4.8.3. By End User
  - 6.2.4.9. Rest of Europe
  - 6.2.4.9.1. By Solution
  - 6.2.4.9.2. By Automation Type
  - 6.2.4.9.3. By End User

7. Asia Pacific Automated Machine Learning (AutoML) Market

7.1. Market Size & Forecast, 2019-2029
- 7.1.1. By Value (USD Billion)
7.2. Market Share & Forecast
- 7.2.1. By Solution
- 7.2.2. By Automation Type
- 7.2.3. By End User
- 7.2.4. By Country
  - 7.2.4.1. China
  - 7.2.4.1.1. By Solution
  - 7.2.4.1.2. By Automation Type
  - 7.2.4.1.3. By End User
  - 7.2.4.2. India
  - 7.2.4.2.1. By Solution
  - 7.2.4.2.2. By Automation Type
  - 7.2.4.2.3. By End User
  - 7.2.4.3. Japan
  - 7.2.4.3.1. By Solution
  - 7.2.4.3.2. By Automation Type
  - 7.2.4.3.3. By End User
  - 7.2.4.4. South Korea
  - 7.2.4.4.1. By Solution
  - 7.2.4.4.2. By Automation Type
  - 7.2.4.4.3. By End User
  - 7.2.4.5. Australia & New Zealand
  - 7.2.4.5.1. By Solution
  - 7.2.4.5.2. By Automation Type
  - 7.2.4.5.3. By End User
  - 7.2.4.6. Indonesia
  - 7.2.4.6.1. By Solution
  - 7.2.4.6.2. By Automation Type
  - 7.2.4.6.3. By End User
  - 7.2.4.7. Malaysia
  - 7.2.4.7.1. By Solution
  - 7.2.4.7.2. By Automation Type
  - 7.2.4.7.3. By End User
  - 7.2.4.8. Singapore
  - 7.2.4.8.1. By Solution
  - 7.2.4.8.2. By Automation Type
  - 7.2.4.8.3. By End User
  - 7.2.4.9. Vietnam
  - 7.2.4.9.1. By Solution
  - 7.2.4.9.2. By Automation Type
  - 7.2.4.9.3. By End User
  - 7.2.4.10. Rest of APAC
  - 7.2.4.10.1. By Solution
  - 7.2.4.10.2. By Automation Type
  - 7.2.4.10.3. By End User

8. Latin America Automated Machine Learning (AutoML) Market

8.1. Market Size & Forecast, 2019-2029
- 8.1.1. By Value (USD Billion)
8.2. Market Share & Forecast
- 8.2.1. By Solution
- 8.2.2. By Automation Type
- 8.2.3. By End User
- 8.2.4. By Country
  - 8.2.4.1. Brazil
  - 8.2.4.1.1. By Solution
  - 8.2.4.1.2. By Automation Type
  - 8.2.4.1.3. By End User
  - 8.2.4.2. Mexico
  - 8.2.4.2.1. By Solution
  - 8.2.4.2.2. By Automation Type
  - 8.2.4.2.3. By End User
  - 8.2.4.3. Argentina
  - 8.2.4.3.1. By Solution
  - 8.2.4.3.2. By Automation Type
  - 8.2.4.3.3. By End User
  - 8.2.4.4. Peru
  - 8.2.4.4.1. By Solution
  - 8.2.4.4.2. By Automation Type
  - 8.2.4.4.3. By End User
  - 8.2.4.5. Rest of LATAM
  - 8.2.4.5.1. By Solution
  - 8.2.4.5.2. By Automation Type
  - 8.2.4.5.3. By End User

9. Middle East & Africa Automated Machine Learning (AutoML) Market

9.1. Market Size & Forecast, 2019-2029
- 9.1.1. By Value (USD Billion)
9.2. Market Share & Forecast
- 9.2.1. By Solution
- 9.2.2. By Automation Type
- 9.2.3. By End User
- 9.2.4. By Country
  - 9.2.4.1. Saudi Arabia
  - 9.2.4.1.1. By Solution
  - 9.2.4.1.2. By Automation Type
  - 9.2.4.1.3. By End User
  - 9.2.4.2. UAE
  - 9.2.4.2.1. By Solution
  - 9.2.4.2.2. By Automation Type
  - 9.2.4.2.3. By End User
  - 9.2.4.3. Qatar
  - 9.2.4.3.1. By Solution
  - 9.2.4.3.2. By Automation Type
  - 9.2.4.3.3. By End User
  - 9.2.4.4. Kuwait
  - 9.2.4.4.1. By Solution
  - 9.2.4.4.2. By Automation Type
  - 9.2.4.4.3. By End User
  - 9.2.4.5. South Africa
  - 9.2.4.5.1. By Solution
  - 9.2.4.5.2. By Automation Type
  - 9.2.4.5.3. By End User
  - 9.2.4.6. Nigeria
  - 9.2.4.6.1. By Solution
  - 9.2.4.6.2. By Automation Type
  - 9.2.4.6.3. By End User
  - 9.2.4.7. Algeria
  - 9.2.4.7.1. By Solution
  - 9.2.4.7.2. By Automation Type
  - 9.2.4.7.3. By End User
  - 9.2.4.8. Rest of MEA
  - 9.2.4.8.1. By Solution
  - 9.2.4.8.2. By Automation Type
  - 9.2.4.8.3. By End User

10. Competitive Landscape

10.1. List of Key Players and Their Offerings
10.2. Global Automated Machine Learning (AutoML) Company Market Share Analysis, 2022
10.3. Competitive Benchmarking, By Operating Parameters
10.4. Key Strategic Developments (Mergers, Acquisitions, Partnerships, etc.)

11. Impact of Covid-19 on Global Automated Machine Learning (AutoML) Market

12. Company Profile (Company Overview, Financial Matrix, Competitive Landscape, Key Personnel, Key Competitors, Contact Address, Strategic Outlook, SWOT Analysis)

12.1. DataRobot Inc.
12.2. Amazon web services Inc.
12.3. dotData Inc.
12.4. IBM Corporation
12.5. Dataiku
12.6. SAS Institute Inc.
12.7. Microsoft Corporation
12.8. Google LLC (Alphabet Inc.)
12.9. H2O.ai
12.10. Aible Inc.
12.11. Other Prominent Players

13. Key Strategic Recommendations

14. Research Methodology

14.1. Qualitative Research
- 14.1.1. Primary & Secondary Research
14.2. Quantitative Research
14.3. Market Breakdown & Data Triangulation
- 14.3.1. Secondary Research
- 14.3.2. Primary Research
14.4. Breakdown of Primary Research Respondents, By Region
14.5. Assumptions & Limitations