Global Machine Learning Operations (MLOps) Market Research Report 2024(Status and Outlook)
Report Overview:
MLOps is the process of taking an experimental Machine Learning model into a production system. The word is a compound of “Machine Learning” and the continuous development practice of DevOps in the software field. Machine Learning models are tested and developed in isolated experimental systems. When an algorithm is ready to be launched, MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems. Similar to DevOps or DataOps approaches, MLOps seeks to increase automation and improve the quality of production models, while also focusing on business and regulatory requirements. While MLOps started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.
The Global Machine Learning Operations (MLOps) Market Size was estimated at USD 1117.64 million in 2023 and is projected to reach USD 9085.74 million by 2029, exhibiting a CAGR of 41.80% during the forecast period.
This report provides a deep insight into the global Machine Learning Operations (MLOps) market covering all its essential aspects. This ranges from a macro overview of the market to micro details of the market size, competitive landscape, development trend, niche market, key market drivers and challenges, SWOT analysis, Porter’s five forces analysis, value chain analysis, etc.
The analysis helps the reader to shape the competition within the industries and strategies for the competitive environment to enhance the potential profit. Furthermore, it provides a simple framework for evaluating and accessing the position of the business organization. The report structure also focuses on the competitive landscape of the Global Machine Learning Operations (MLOps) Market, this report introduces in detail the market share, market performance, product situation, operation situation, etc. of the main players, which helps the readers in the industry to identify the main competitors and deeply understand the competition pattern of the market.
In a word, this report is a must-read for industry players, investors, researchers, consultants, business strategists, and all those who have any kind of stake or are planning to foray into the Machine Learning Operations (MLOps) market in any manner.
Global Machine Learning Operations (MLOps) Market: Market Segmentation Analysis
The research report includes specific segments by region (country), manufacturers, Type, and Application. Market segmentation creates subsets of a market based on product type, end-user or application, Geographic, and other factors. By understanding the market segments, the decision-maker can leverage this targeting in the product, sales, and marketing strategies. Market segments can power your product development cycles by informing how you create product offerings for different segments.
Key Company
IBM
DataRobot
SAS
Microsoft
Amazon
Dataiku
Databricks
HPE
Lguazio
ClearML
Modzy
Comet
Cloudera
Paperpace
Valohai
Market Segmentation (by Type)
On-premise
Cloud
Others
Market Segmentation (by Application)
BFSI
Healthcare
Retail
Manufacturing
Public Sector
Others
Geographic Segmentation
- North America (USA, Canada, Mexico)
- Europe (Germany, UK, France, Russia, Italy, Rest of Europe)
- Asia-Pacific (China, Japan, South Korea, India, Southeast Asia, Rest of Asia-Pacific)
- South America (Brazil, Argentina, Columbia, Rest of South America)
- The Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, South Africa, Rest of MEA)
- Industry drivers, restraints, and opportunities covered in the study
- Neutral perspective on the market performance
- Recent industry trends and developments
- Competitive landscape & strategies of key players
- Potential & niche segments and regions exhibiting promising growth covered
- Historical, current, and projected market size, in terms of value
- In-depth analysis of the Machine Learning Operations (MLOps) Market
- Overview of the regional outlook of the Machine Learning Operations (MLOps) Market:
- Access to date statistics compiled by our researchers. These provide you with historical and forecast data, which is analyzed to tell you why your market is set to change
- This enables you to anticipate market changes to remain ahead of your competitors
- You will be able to copy data from the Excel spreadsheet straight into your marketing plans, business presentations, or other strategic documents
- The concise analysis, clear graph, and table format will enable you to pinpoint the information you require quickly
- Provision of market value (USD Billion) data for each segment and sub-segment
- Indicates the region and segment that is expected to witness the fastest growth as well as to dominate the market
- Analysis by geography highlighting the consumption of the product/service in the region as well as indicating the factors that are affecting the market within each region
- Competitive landscape which incorporates the market ranking of the major players, along with new service/product launches, partnerships, business expansions, and acquisitions in the past five years of companies profiled
- Extensive company profiles comprising of company overview, company insights, product benchmarking, and SWOT analysis for the major market players
- The current as well as the future market outlook of the industry concerning recent developments which involve growth opportunities and drivers as well as challenges and restraints of both emerging as well as developed regions
- Includes in-depth analysis of the market from various perspectives through Porter’s five forces analysis
- Provides insight into the market through Value Chain
- Market dynamics scenario, along with growth opportunities of the market in the years to come
- 6-month post-sales analyst support
In case of any queries or customization requirements, please connect with our sales team, who will ensure that your requirements are met.
Note: this report may need to undergo a final check or review and this could take about 48 hours.
Chapter Outline
Chapter 1 mainly introduces the statistical scope of the report, market division standards, and market research methods.
Chapter 2 is an executive summary of different market segments (by region, product type, application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the Machine Learning Operations (MLOps) Market and its likely evolution in the short to mid-term, and long term.
Chapter 3 makes a detailed analysis of the Market's Competitive Landscape of the market and provides the market share, capacity, output, price, latest development plan, merger, and acquisition information of the main manufacturers in the market.
Chapter 4 is the analysis of the whole market industrial chain, including the upstream and downstream of the industry, as well as Porter's five forces analysis.
Chapter 5 introduces the latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.
Chapter 6 provides the analysis of various market segments according to product types, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 7 provides the analysis of various market segments according to application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 8 provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 9 introduces the basic situation of the main companies in the market in detail, including product sales revenue, sales volume, price, gross profit margin, market share, product introduction, recent development, etc.
Chapter 10 provides a quantitative analysis of the market size and development potential of each region in the next five years.
Chapter 11 provides a quantitative analysis of the market size and development potential of each market segment (product type and application) in the next five years.
Chapter 12 is the main points and conclusions of the report.
- 1 Research Methodology and Statistical Scope
- 1.1 Market Definition and Statistical Scope of Machine Learning Operations (MLOps)
- 1.2 Key Market Segments
- 1.2.1 Machine Learning Operations (MLOps) Segment by Type
- 1.2.2 Machine Learning Operations (MLOps) Segment by Application
- 1.3 Methodology & Sources of Information
- 1.3.1 Research Methodology
- 1.3.2 Research Process
- 1.3.3 Market Breakdown and Data Triangulation
- 1.3.4 Base Year
- 1.3.5 Report Assumptions & Caveats
- 2 Machine Learning Operations (MLOps) Market Overview
- 2.1 Global Market Overview
- 2.2 Market Segment Executive Summary
- 2.3 Global Market Size by Region
- 3 Machine Learning Operations (MLOps) Market Competitive Landscape
- 3.1 Global Machine Learning Operations (MLOps) Revenue Market Share by Company (2019-2024)
- 3.2 Machine Learning Operations (MLOps) Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
- 3.3 Company Machine Learning Operations (MLOps) Market Size Sites, Area Served, Product Type
- 3.4 Machine Learning Operations (MLOps) Market Competitive Situation and Trends
- 3.4.1 Machine Learning Operations (MLOps) Market Concentration Rate
- 3.4.2 Global 5 and 10 Largest Machine Learning Operations (MLOps) Players Market Share by Revenue
- 3.4.3 Mergers & Acquisitions, Expansion
- 4 Machine Learning Operations (MLOps) Value Chain Analysis
- 4.1 Machine Learning Operations (MLOps) Value Chain Analysis
- 4.2 Midstream Market Analysis
- 4.3 Downstream Customer Analysis
- 5 The Development and Dynamics of Machine Learning Operations (MLOps) Market
- 5.1 Key Development Trends
- 5.2 Driving Factors
- 5.3 Market Challenges
- 5.4 Market Restraints
- 5.5 Industry News
- 5.5.1 Mergers & Acquisitions
- 5.5.2 Expansions
- 5.5.3 Collaboration/Supply Contracts
- 5.6 Industry Policies
- 6 Machine Learning Operations (MLOps) Market Segmentation by Type
- 6.1 Evaluation Matrix of Segment Market Development Potential (Type)
- 6.2 Global Machine Learning Operations (MLOps) Market Size Market Share by Type (2019-2024)
- 6.3 Global Machine Learning Operations (MLOps) Market Size Growth Rate by Type (2019-2024)
- 7 Machine Learning Operations (MLOps) Market Segmentation by Application
- 7.1 Evaluation Matrix of Segment Market Development Potential (Application)
- 7.2 Global Machine Learning Operations (MLOps) Market Size (M USD) by Application (2019-2024)
- 7.3 Global Machine Learning Operations (MLOps) Market Size Growth Rate by Application (2019-2024)
- 8 Machine Learning Operations (MLOps) Market Segmentation by Region
- 8.1 Global Machine Learning Operations (MLOps) Market Size by Region
- 8.1.1 Global Machine Learning Operations (MLOps) Market Size by Region
- 8.1.2 Global Machine Learning Operations (MLOps) Market Size Market Share by Region
- 8.2 North America
- 8.2.1 North America Machine Learning Operations (MLOps) Market Size by Country
- 8.2.2 U.S.
- 8.2.3 Canada
- 8.2.4 Mexico
- 8.3 Europe
- 8.3.1 Europe Machine Learning Operations (MLOps) Market Size by Country
- 8.3.2 Germany
- 8.3.3 France
- 8.3.4 U.K.
- 8.3.5 Italy
- 8.3.6 Russia
- 8.4 Asia Pacific
- 8.4.1 Asia Pacific Machine Learning Operations (MLOps) Market Size by Region
- 8.4.2 China
- 8.4.3 Japan
- 8.4.4 South Korea
- 8.4.5 India
- 8.4.6 Southeast Asia
- 8.5 South America
- 8.5.1 South America Machine Learning Operations (MLOps) Market Size by Country
- 8.5.2 Brazil
- 8.5.3 Argentina
- 8.5.4 Columbia
- 8.6 Middle East and Africa
- 8.6.1 Middle East and Africa Machine Learning Operations (MLOps) Market Size by Region
- 8.6.2 Saudi Arabia
- 8.6.3 UAE
- 8.6.4 Egypt
- 8.6.5 Nigeria
- 8.6.6 South Africa
- 9 Key Companies Profile
- 9.1 IBM
- 9.1.1 IBM Machine Learning Operations (MLOps) Basic Information
- 9.1.2 IBM Machine Learning Operations (MLOps) Product Overview
- 9.1.3 IBM Machine Learning Operations (MLOps) Product Market Performance
- 9.1.4 IBM Machine Learning Operations (MLOps) SWOT Analysis
- 9.1.5 IBM Business Overview
- 9.1.6 IBM Recent Developments
- 9.2 DataRobot
- 9.2.1 DataRobot Machine Learning Operations (MLOps) Basic Information
- 9.2.2 DataRobot Machine Learning Operations (MLOps) Product Overview
- 9.2.3 DataRobot Machine Learning Operations (MLOps) Product Market Performance
- 9.2.4 IBM Machine Learning Operations (MLOps) SWOT Analysis
- 9.2.5 DataRobot Business Overview
- 9.2.6 DataRobot Recent Developments
- 9.3 SAS
- 9.3.1 SAS Machine Learning Operations (MLOps) Basic Information
- 9.3.2 SAS Machine Learning Operations (MLOps) Product Overview
- 9.3.3 SAS Machine Learning Operations (MLOps) Product Market Performance
- 9.3.4 IBM Machine Learning Operations (MLOps) SWOT Analysis
- 9.3.5 SAS Business Overview
- 9.3.6 SAS Recent Developments
- 9.4 Microsoft
- 9.4.1 Microsoft Machine Learning Operations (MLOps) Basic Information
- 9.4.2 Microsoft Machine Learning Operations (MLOps) Product Overview
- 9.4.3 Microsoft Machine Learning Operations (MLOps) Product Market Performance
- 9.4.4 Microsoft Business Overview
- 9.4.5 Microsoft Recent Developments
- 9.5 Amazon
- 9.5.1 Amazon Machine Learning Operations (MLOps) Basic Information
- 9.5.2 Amazon Machine Learning Operations (MLOps) Product Overview
- 9.5.3 Amazon Machine Learning Operations (MLOps) Product Market Performance
- 9.5.4 Amazon Business Overview
- 9.5.5 Amazon Recent Developments
- 9.6 Google
- 9.6.1 Google Machine Learning Operations (MLOps) Basic Information
- 9.6.2 Google Machine Learning Operations (MLOps) Product Overview
- 9.6.3 Google Machine Learning Operations (MLOps) Product Market Performance
- 9.6.4 Google Business Overview
- 9.6.5 Google Recent Developments
- 9.7 Dataiku
- 9.7.1 Dataiku Machine Learning Operations (MLOps) Basic Information
- 9.7.2 Dataiku Machine Learning Operations (MLOps) Product Overview
- 9.7.3 Dataiku Machine Learning Operations (MLOps) Product Market Performance
- 9.7.4 Dataiku Business Overview
- 9.7.5 Dataiku Recent Developments
- 9.8 Databricks
- 9.8.1 Databricks Machine Learning Operations (MLOps) Basic Information
- 9.8.2 Databricks Machine Learning Operations (MLOps) Product Overview
- 9.8.3 Databricks Machine Learning Operations (MLOps) Product Market Performance
- 9.8.4 Databricks Business Overview
- 9.8.5 Databricks Recent Developments
- 9.9 HPE
- 9.9.1 HPE Machine Learning Operations (MLOps) Basic Information
- 9.9.2 HPE Machine Learning Operations (MLOps) Product Overview
- 9.9.3 HPE Machine Learning Operations (MLOps) Product Market Performance
- 9.9.4 HPE Business Overview
- 9.9.5 HPE Recent Developments
- 9.10 Lguazio
- 9.10.1 Lguazio Machine Learning Operations (MLOps) Basic Information
- 9.10.2 Lguazio Machine Learning Operations (MLOps) Product Overview
- 9.10.3 Lguazio Machine Learning Operations (MLOps) Product Market Performance
- 9.10.4 Lguazio Business Overview
- 9.10.5 Lguazio Recent Developments
- 9.11 ClearML
- 9.11.1 ClearML Machine Learning Operations (MLOps) Basic Information
- 9.11.2 ClearML Machine Learning Operations (MLOps) Product Overview
- 9.11.3 ClearML Machine Learning Operations (MLOps) Product Market Performance
- 9.11.4 ClearML Business Overview
- 9.11.5 ClearML Recent Developments
- 9.12 Modzy
- 9.12.1 Modzy Machine Learning Operations (MLOps) Basic Information
- 9.12.2 Modzy Machine Learning Operations (MLOps) Product Overview
- 9.12.3 Modzy Machine Learning Operations (MLOps) Product Market Performance
- 9.12.4 Modzy Business Overview
- 9.12.5 Modzy Recent Developments
- 9.13 Comet
- 9.13.1 Comet Machine Learning Operations (MLOps) Basic Information
- 9.13.2 Comet Machine Learning Operations (MLOps) Product Overview
- 9.13.3 Comet Machine Learning Operations (MLOps) Product Market Performance
- 9.13.4 Comet Business Overview
- 9.13.5 Comet Recent Developments
- 9.14 Cloudera
- 9.14.1 Cloudera Machine Learning Operations (MLOps) Basic Information
- 9.14.2 Cloudera Machine Learning Operations (MLOps) Product Overview
- 9.14.3 Cloudera Machine Learning Operations (MLOps) Product Market Performance
- 9.14.4 Cloudera Business Overview
- 9.14.5 Cloudera Recent Developments
- 9.15 Paperpace
- 9.15.1 Paperpace Machine Learning Operations (MLOps) Basic Information
- 9.15.2 Paperpace Machine Learning Operations (MLOps) Product Overview
- 9.15.3 Paperpace Machine Learning Operations (MLOps) Product Market Performance
- 9.15.4 Paperpace Business Overview
- 9.15.5 Paperpace Recent Developments
- 9.16 Valohai
- 9.16.1 Valohai Machine Learning Operations (MLOps) Basic Information
- 9.16.2 Valohai Machine Learning Operations (MLOps) Product Overview
- 9.16.3 Valohai Machine Learning Operations (MLOps) Product Market Performance
- 9.16.4 Valohai Business Overview
- 9.16.5 Valohai Recent Developments
- 10 Machine Learning Operations (MLOps) Regional Market Forecast
- 10.1 Global Machine Learning Operations (MLOps) Market Size Forecast
- 10.2 Global Machine Learning Operations (MLOps) Market Forecast by Region
- 10.2.1 North America Market Size Forecast by Country
- 10.2.2 Europe Machine Learning Operations (MLOps) Market Size Forecast by Country
- 10.2.3 Asia Pacific Machine Learning Operations (MLOps) Market Size Forecast by Region
- 10.2.4 South America Machine Learning Operations (MLOps) Market Size Forecast by Country
- 10.2.5 Middle East and Africa Forecasted Consumption of Machine Learning Operations (MLOps) by Country
- 11 Forecast Market by Type and by Application (2025-2030)
- 11.1 Global Machine Learning Operations (MLOps) Market Forecast by Type (2025-2030)
- 11.2 Global Machine Learning Operations (MLOps) Market Forecast by Application (2025-2030)
- 12 Conclusion and Key Findings
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