Machine Learning Operations Market by Component, Deployment, Organization Size, End-User

List of Tables

The Machine Learning Operations Market is projected to reach USD 28.26 billion by 2030 from USD 2.39 billion in 2022, at a CAGR of 36.15% during the forecast period.

Global Machine Learning Operations Market

KEY MARKET STATISTICS
Base Year Value [2022]	USD 2.39 billion
Estimated Year Value [2023]	USD 3.24 billion
Forecast Year Value [2030]	USD 28.26 billion
CAGR (%)	36.15%

Machine Learning Operations Market - IMG1

Market Segmentation & Coverage:

This research report analyzes various sub-markets, forecasts revenues, and examines emerging trends in each category to provide a comprehensive outlook on the Machine Learning Operations Market.

Based on Component, market is studied across Services and Software. The Services is projected to witness significant market share during forecast period.

Based on Deployment, market is studied across Cloud and On-Premise. The Cloud is projected to witness significant market share during forecast period.

Based on Organization Size, market is studied across Large Enterprises and SMEs. The Large Enterprises is projected to witness significant market share during forecast period.

Based on End-User, market is studied across Aerospace & Defense, Automotive & Transportation, Banking, Financial Services & Insurance, Building, Construction & Real Estate, Consumer Goods & Retail, Education, Energy & Utilities, Government & Public Sector, Healthcare & Life Sciences, Information Technology & Telecommunication, Manufacturing, Media & Entertainment, and Travel & Hospitality. The Banking, Financial Services & Insurance is projected to witness significant market share during forecast period.

Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom. The Asia-Pacific is projected to witness significant market share during forecast period.

Market Statistics:

The report provides market sizing and forecasts across 7 major currencies - USD, EUR, JPY, GBP, AUD, CAD, and CHF; multiple currency support helps organization leaders to make well-informed decisions. In this report, 2018 to 2021 are considered as historical years, 2022 is base year, 2023 is estimated year, and years from 2024 to 2030 are considered as forecast period.

FPNV Positioning Matrix:

The FPNV Positioning Matrix is an indispensable tool for assessing the Machine Learning Operations Market. It comprehensively evaluates vendors, analyzing key metrics related to Business Strategy and Product Satisfaction. This enables users to make informed decisions tailored to their specific needs. Through advanced analysis, vendors are categorized into four distinct quadrants, each representing a different level of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V). Be assured that this insightful framework empowers decision-makers to navigate the market with confidence.

Market Share Analysis:

The Market Share Analysis offers invaluable insights into the vendor landscape Machine Learning Operations Market. By evaluating their impact on overall revenue, customer base, and other key metrics, we provide companies with a comprehensive understanding of their performance and the competitive environment they confront. This analysis also uncovers the level of competition in terms of market share acquisition, fragmentation, dominance, and industry consolidation during the study period.

Key Company Profiles:

The report delves into recent significant developments in the Machine Learning Operations Market, highlighting leading vendors and their innovative profiles. These include Addepto Sp. z o. o., Alibaba Cloud International, Allegro Artificial Intelligence Ltd., Amazon Web Services, Inc., Anyscale, Inc., BigML Inc., Canonical Ltd., Dataiku, DataRobot, Inc., Domino Data Lab, Inc., Gathr Data Inc., Google LLC by Alphabet Inc., Grid Dynamics Holdings, Inc., H2O.ai, Inc., Hewlett Packard Enterprise Company, Iguazio Ltd. by McKinsey & Company, International Business Machines Corporation, Microsoft Corporation, Neal Analytics, Neptune Labs, Inc., Neuro Inc., Oracle Corporation, Runai Labs Ltd., SAP SE, SAS Institute Inc., Tredence Analytics Solutions Pvt. Ltd., understandAI GmbH, Valohai, Virtusa Corporation, and Weights and Biases, Inc..

The report offers valuable insights on the following aspects:

1. Market Penetration: It provides comprehensive information about key players' market dynamics and offerings.

2. Market Development: In-depth analysis of emerging markets and penetration across mature market segments, highlighting lucrative opportunities.

3. Market Diversification: Detailed information about new product launches, untapped geographies, recent developments, and investments.

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

5. Product Development & Innovation: 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 for the Machine Learning Operations Market?

2. Which products, segments, applications, and areas hold the highest investment potential in the Machine Learning Operations Market?

3. What is the competitive strategic window for identifying opportunities in the Machine Learning Operations Market?

4. What are the latest technology trends and regulatory frameworks in the Machine Learning Operations Market?

5. What is the market share of the leading vendors in the Machine Learning Operations Market?

6. Which modes and strategic moves are suitable for entering the Machine Learning Operations Market?

Product Code: MRR-961BA04A2E4F

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. Machine Learning Operations Market, by Region

5. Market Insights

5.1. Market Dynamics
- 5.1.1. Drivers
  - 5.1.1.1. Increasing utilization of machine learning in the manufacturing sector
  - 5.1.1.2. Government initiatives to digitalize and automate end-user sectors to boost productivity
  - 5.1.1.3. Growing focus on standardization of machine learning processes for better management
- 5.1.2. Restraints
  - 5.1.2.1. Issues associated with data management due to discrepancies
- 5.1.3. Opportunities
  - 5.1.3.1. Continuous improvements in machine learning operations and development of new solutions
  - 5.1.3.2. New investments in smart factory and smart manufacturing technologies
- 5.1.4. Challenges
  - 5.1.4.1. Limited availability of skilled and trained professionals
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. Machine Learning Operations Market, by Component

6.1. Introduction
6.2. Services
6.3. Software

7. Machine Learning Operations Market, by Deployment

7.1. Introduction
7.2. Cloud
7.3. On-Premise

8. Machine Learning Operations Market, by Organization Size

8.1. Introduction
8.2. Large Enterprises
8.3. SMEs

9. Machine Learning Operations Market, by End-User

9.1. Introduction
9.2. Aerospace & Defense
9.3. Automotive & Transportation
9.4. Banking, Financial Services & Insurance
9.5. Building, Construction & Real Estate
9.6. Consumer Goods & Retail
9.7. Education
9.8. Energy & Utilities
9.9. Government & Public Sector
9.10. Healthcare & Life Sciences
9.11. Information Technology & Telecommunication
9.12. Manufacturing
9.13. Media & Entertainment
9.14. Travel & Hospitality

10. Americas Machine Learning Operations Market

10.1. Introduction
10.2. Argentina
10.3. Brazil
10.4. Canada
10.5. Mexico
10.6. United States

11. Asia-Pacific Machine Learning Operations Market

11.1. Introduction
11.2. Australia
11.3. China
11.4. India
11.5. Indonesia
11.6. Japan
11.7. Malaysia
11.8. Philippines
11.9. Singapore
11.10. South Korea
11.11. Taiwan
11.12. Thailand
11.13. Vietnam

12. Europe, Middle East & Africa Machine Learning Operations Market

12.1. Introduction
12.2. Denmark
12.3. Egypt
12.4. Finland
12.5. France
12.6. Germany
12.7. Israel
12.8. Italy
12.9. Netherlands
12.10. Nigeria
12.11. Norway
12.12. Poland
12.13. Qatar
12.14. Russia
12.15. Saudi Arabia
12.16. South Africa
12.17. Spain
12.18. Sweden
12.19. Switzerland
12.20. Turkey
12.21. United Arab Emirates
12.22. United Kingdom

13. Competitive Landscape

13.1. FPNV Positioning Matrix
13.2. Market Share Analysis, By Key Player
13.3. Competitive Scenario Analysis, By Key Player

14. Competitive Portfolio

14.1. Key Company Profiles
- 14.1.1. Addepto Sp. z o. o.
- 14.1.2. Alibaba Cloud International
- 14.1.3. Allegro Artificial Intelligence Ltd.
- 14.1.4. Amazon Web Services, Inc.
- 14.1.5. Anyscale, Inc.
- 14.1.6. BigML Inc.
- 14.1.7. Canonical Ltd.
- 14.1.8. Dataiku
- 14.1.9. DataRobot, Inc.
- 14.1.10. Domino Data Lab, Inc.
- 14.1.11. Gathr Data Inc.
- 14.1.12. Google LLC by Alphabet Inc.
- 14.1.13. Grid Dynamics Holdings, Inc.
- 14.1.14. H2O.ai, Inc.
- 14.1.15. Hewlett Packard Enterprise Company
- 14.1.16. Iguazio Ltd. by McKinsey & Company
- 14.1.17. International Business Machines Corporation
- 14.1.18. Microsoft Corporation
- 14.1.19. Neal Analytics
- 14.1.20. Neptune Labs, Inc.
- 14.1.21. Neuro Inc.
- 14.1.22. Oracle Corporation
- 14.1.23. Runai Labs Ltd.
- 14.1.24. SAP SE
- 14.1.25. SAS Institute Inc.
- 14.1.26. Tredence Analytics Solutions Pvt. Ltd.
- 14.1.27. understandAI GmbH
- 14.1.28. Valohai
- 14.1.29. Virtusa Corporation
- 14.1.30. Weights and Biases, Inc.
14.2. Key Product Portfolio

15. Appendix

15.1. Discussion Guide
15.2. License & Pricing