Automated Machine Learning Market by Automation Type, Deployment, Application

Description

List of Tables

[186 Pages Report] The Automated Machine Learning Market size was estimated at USD 1.63 billion in 2023 and expected to reach USD 2.21 billion in 2024, at a CAGR 35.70% to reach USD 13.88 billion by 2030.

Global Automated Machine Learning Market

KEY MARKET STATISTICS
Base Year [2023]	USD 1.63 billion
Estimated Year [2024]	USD 2.21 billion
Forecast Year [2030]	USD 13.88 billion
CAGR (%)	35.70%

Automated Machine Learning Market - IMG1

FPNV Positioning Matrix

The FPNV Positioning Matrix is pivotal in evaluating the Automated Machine Learning Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V).

Market Share Analysis

The Market Share Analysis is a comprehensive tool that provides an insightful and in-depth examination of the current state of vendors in the Automated Machine Learning Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.

Key Company Profiles

The report delves into recent significant developments in the Automated Machine Learning Market, highlighting leading vendors and their innovative profiles. These include Aible, Inc., Akkio Inc., Altair Engineering Inc., Alteryx, Amazon Web Services, Inc., Automated Machine Learning Ltd., BigML, Inc., Databricks, Inc., Dataiku, DataRobot, Inc., Google LLC by Alphabet Inc., H2O.ai, Inc., Hewlett Packard Enterprise Company, InData Labs Group Limited, Intel Corporation, International Business Machines Corporation, Microsoft Corporation, Oracle Corporation, QlikTech International AB, Runai Labs Ltd., Salesforce, Inc., SAS Institute Inc., ServiceNow, Inc., SparkCognition, Inc., STMicroelectronics, Tata Consultancy Services Limited, TAZI AI, Tellius, Inc., Weidmuller Limited, Wolfram, and Yellow.ai.

Market Segmentation & Coverage

This research report categorizes the Automated Machine Learning Market to forecast the revenues and analyze trends in each of the following sub-markets:

Automation Type
- Data Processing
- Feature Engineering
- Modeling
- Visualization
Deployment
- Cloud
- On-premises
Application
- Automotive, Transportations, and Logistics
- Banking, Financial Services, and Insurance
- Government & Defense
- Healthcare & Life Sciences
- It & Telecommunications
- Media & Entertainment
Region
- Americas
  - Argentina
  - Brazil
  - Canada
  - Mexico
  - United States
    - California
    - Florida
    - Illinois
    - New York
    - Ohio
    - Pennsylvania
    - Texas
- Asia-Pacific
  - Australia
  - China
  - India
  - Indonesia
  - Japan
  - Malaysia
  - Philippines
  - Singapore
  - South Korea
  - Taiwan
  - Thailand
  - Vietnam
- Europe, Middle East & Africa
  - Denmark
  - Egypt
  - Finland
  - France
  - Germany
  - Israel
  - Italy
  - Netherlands
  - Nigeria
  - Norway
  - Poland
  - Qatar
  - Russia
  - Saudi Arabia
  - South Africa
  - Spain
  - Sweden
  - Switzerland
  - Turkey
  - United Arab Emirates
  - United Kingdom

The report offers valuable insights on the following aspects:

1. Market Penetration: It presents comprehensive information on the market provided by key players.

2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.

3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.

4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.

5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.

The report addresses key questions such as:

1. What is the market size and forecast of the Automated Machine Learning Market?

2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the Automated Machine Learning Market?

3. What are the technology trends and regulatory frameworks in the Automated Machine Learning Market?

4. What is the market share of the leading vendors in the Automated Machine Learning Market?

5. Which modes and strategic moves are suitable for entering the Automated Machine Learning Market?

Product Code: MRR-961BA04A2DBA

1. Preface

1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Limitations
1.7. Assumptions
1.8. Stakeholders

2. Research Methodology

2.1. Define: Research Objective
2.2. Determine: Research Design
2.3. Prepare: Research Instrument
2.4. Collect: Data Source
2.5. Analyze: Data Interpretation
2.6. Formulate: Data Verification
2.7. Publish: Research Report
2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

4.1. Introduction
4.2. Automated Machine Learning Market, by Region

5. Market Insights

5.1. Market Dynamics
- 5.1.1. Drivers
  - 5.1.1.1. Increasing demand for data-driven insights for decision-making
  - 5.1.1.2. Expanding democratization of machine learning capabilities
- 5.1.2. Restraints
  - 5.1.2.1. Interpretability and transparency issues associated with AutoML platforms
- 5.1.3. Opportunities
  - 5.1.3.1. Advancements in artificial intelligence (AI) and machine learning (ML) technologies
  - 5.1.3.2. Growing integration of AutoML with DevOps practices that enhance the development of machine learning models
- 5.1.4. Challenges
  - 5.1.4.1. Security and privacy concerns of AutoML platforms
5.2. Market Segmentation Analysis
5.3. Market Trend Analysis
5.4. Cumulative Impact of High Inflation
5.5. Porter's Five Forces Analysis
- 5.5.1. Threat of New Entrants
- 5.5.2. Threat of Substitutes
- 5.5.3. Bargaining Power of Customers
- 5.5.4. Bargaining Power of Suppliers
- 5.5.5. Industry Rivalry
5.6. Value Chain & Critical Path Analysis
5.7. Regulatory Framework

6. Automated Machine Learning Market, by Automation Type

6.1. Introduction
6.2. Data Processing
6.3. Feature Engineering
6.4. Modeling
6.5. Visualization

7. Automated Machine Learning Market, by Deployment

7.1. Introduction
7.2. Cloud
7.3. On-premises

8. Automated Machine Learning Market, by Application

8.1. Introduction
8.2. Automotive, Transportations, and Logistics
8.3. Banking, Financial Services, and Insurance
8.4. Government & Defense
8.5. Healthcare & Life Sciences
8.6. It & Telecommunications
8.7. Media & Entertainment

9. Americas Automated Machine Learning Market

9.1. Introduction
9.2. Argentina
9.3. Brazil
9.4. Canada
9.5. Mexico
9.6. United States

10. Asia-Pacific Automated Machine Learning Market

10.1. Introduction
10.2. Australia
10.3. China
10.4. India
10.5. Indonesia
10.6. Japan
10.7. Malaysia
10.8. Philippines
10.9. Singapore
10.10. South Korea
10.11. Taiwan
10.12. Thailand
10.13. Vietnam

11. Europe, Middle East & Africa Automated Machine Learning Market

11.1. Introduction
11.2. Denmark
11.3. Egypt
11.4. Finland
11.5. France
11.6. Germany
11.7. Israel
11.8. Italy
11.9. Netherlands
11.10. Nigeria
11.11. Norway
11.12. Poland
11.13. Qatar
11.14. Russia
11.15. Saudi Arabia
11.16. South Africa
11.17. Spain
11.18. Sweden
11.19. Switzerland
11.20. Turkey
11.21. United Arab Emirates
11.22. United Kingdom

12. Competitive Landscape

12.1. FPNV Positioning Matrix
12.2. Market Share Analysis, By Key Player
12.3. Competitive Scenario Analysis, By Key Player

13. Competitive Portfolio

13.1. Key Company Profiles
- 13.1.1. Aible, Inc.
- 13.1.2. Akkio Inc.
- 13.1.3. Altair Engineering Inc.
- 13.1.4. Alteryx
- 13.1.5. Amazon Web Services, Inc.
- 13.1.6. Automated Machine Learning Ltd.
- 13.1.7. BigML, Inc.
- 13.1.8. Databricks, Inc.
- 13.1.9. Dataiku
- 13.1.10. DataRobot, Inc.
- 13.1.11. Google LLC by Alphabet Inc.
- 13.1.12. H2O.ai, Inc.
- 13.1.13. Hewlett Packard Enterprise Company
- 13.1.14. InData Labs Group Limited
- 13.1.15. Intel Corporation
- 13.1.16. International Business Machines Corporation
- 13.1.17. Microsoft Corporation
- 13.1.18. Oracle Corporation
- 13.1.19. QlikTech International AB
- 13.1.20. Runai Labs Ltd.
- 13.1.21. Salesforce, Inc.
- 13.1.22. SAS Institute Inc.
- 13.1.23. ServiceNow, Inc.
- 13.1.24. SparkCognition, Inc.
- 13.1.25. STMicroelectronics
- 13.1.26. Tata Consultancy Services Limited
- 13.1.27. TAZI AI
- 13.1.28. Tellius, Inc.
- 13.1.29. Weidmuller Limited
- 13.1.30. Wolfram
- 13.1.31. Yellow.ai
13.2. Key Product Portfolio

14. Appendix

14.1. Discussion Guide
14.2. License & Pricing