Global MLOps Market

Global MLOps Market Size, Share, Growth Analysis, By Infrastructure(Data Infrastructure and Model Infrastructure), By Data Management(Data Pipeline Management and Data Versioning) - Industry Forecast 2024-2031


Report ID: SQMIG45D2064 | Region: Global | Published Date: April, 2024
Pages: 197 | Tables: 59 | Figures: 75

Global MLOps Market Insights

Global MLOps Market size was valued at USD 1.10 billion in 2022 and is poised to grow from USD 1.55 billion in 2023 to USD 24.23 billion by 2031, growing at a CAGR of 41% during the forecast period (2024-2031).

MLOps is the technology that empowers production-level machine learning. On the contrary, latest MLOps trends and predictions are emerging to meet the evolving challenges in scaling machine learning. The emerging advanced MLOps applications can solve a variety of human error and quality issues.

Hence, many organizations such as healthcare, IT, retail, and other sectors are adopting MLOps due to its benefits. This factor creates lucrative growth opportunities in the market. Surge in digital and internet penetration around the world is positively impacting the growth of the market.

In addition, increase in adoption of MLOps technology across enterprises to enhance operation & productivity, strengthens the growth of the market for the future. Furthermore, increasing investments in the healthcare sector is expected to provide the lucrative growth opportunities for the market during the forecast period.

Moreover, MLOps help to reduce costs over the entire machine learning lifecycle, creating numerous opportunities for market growth in the upcoming years. However, inaccessible data and data security, rigid business models and lack of engineering skills, hamper the MLOps market growth.

US MLOps Market is poised to grow at a sustainable CAGR for the next forecast year.

Market snapshot - 2024-2031

Global Market Size

USD 1.10 billion

Largest Segment

Model Infrastructure

Fastest Growth

Model Infrastructure

Growth Rate

41% CAGR

Global MLOps Market ($ Bn)
Country Share for North America Region (%)
Global MLOp Market By TypInfrastructure ($ Bn)
Global MLOp Market By Infrastructure (%)

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Global MLOps Market Segmental Analysis

The global MLOps market is segmented by infrastructure, data management, and region. Based on infrastructure, the market can be segmented into data infrastructure and model infrastructure. Based on data management the market is segmented into data pipeline management and data versioning. Based on region, the market is segmented into North America, Europe, Asia Pacific, Middle East and Africa, and Latin America.

MLOps Market Analysis by Infrastructure

The infrastructure segment of MLOps involves setting up and managing the hardware and software components required for machine learning model development, deployment, and maintenance. It consists of two sub-segments:

Data infrastructure is the dominant sub-segment, and involves managing the data storage and processing components required for machine learning. It includes data storage technologies such as data lakes, databases, and data warehouses, along with data processing tools such as ETL (Extract, Transform, Load) pipelines and stream processing systems. Data infrastructure also includes managing the data versioning and tracking systems to keep track of changes made to the data for model training.

Model infrastructure sub-segment is the fastest growing sub-segment and involves managing the infrastructure for model development, deployment, and monitoring. It includes tools for version control, model training, and deployment such as deep learning frameworks, model serving platforms, and model monitoring tools. Model infrastructure also includes setting up the infrastructure for continuous integration and continuous deployment (CI/CD) pipelines for model updates and version control.

MLOps Market Analysis by Data Management

Data pipeline management involves building and maintaining automated workflows that move data from various sources to the machine learning models for training and inference. This will drive the demand in the coming years. For example, data pipeline management may involve building ETL (Extract, Transform, Load) workflows that pull data from a variety of sources such as databases, APIs, and file systems, transform it into a format suitable for machine learning models, and push it into the machine learning infrastructure.

Global MLOp Market By Infrastructure, 2022 (%)

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Global MLOps Market Regional Insights

North America followed by Asia Pacific is one of the leading markers for MLOps in terms of market share. Countries, such as the US and Canada, are adopting ML technology in multiple application areas, propelling the growth of MLOps in this region. In the North American MLOps market, the US is considered one of the major contributors. The presence of prominent technology providers, such as IBM (US), Google (US), Microsoft (US), HPE (US), and AWS (US), is complementing the growth of the market in this region. The presence of such established MLOps companies and the emergence of new start-ups will strengthen the outlook of this region and enable it to witness a significant increase in investments and early adoption of Artificial Intelligence technology.

Global MLOp Market By Region, 2024-2031
  • Largest
  • Fastest

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Global MLOps Market Dynamics

MLOps Market Drivers

Standardizing Ml Processes For Effective Teamwork

  • Manual data reprocessing and collecting are ineffective and might produce unsatisfactory results. MLOps helps for automating the whole ML model workflow. This includes data gathering, model construction, testing, retraining, and development. MLOps help companies save time and reduce error rates. Collaboration is seen between IT and business personnel, as well as data scientists and engineers, for the company-wide adoption of ML models. Businesses can standardize ML operations and establish a standardized language for all participants due to MLOps principles. This reduces compatibility problems and quickens the construction and deployment of modelling processes.

MLOps Market Restraints

Lack Of Expertise

  • In order to gather and integrate the vast volumes of data from multiple internal and external data sources, as well as to merge the data silos, organisations should now use MLOps in data management. However, they are unable to embrace MLOps models due to knowledge gaps and a lack of worker capabilities. Organizations frequently operate in silos; thus, the necessity for MLOps models is becoming increasingly crucial in order to acquire a broader picture of many applications and verticals. Surveys have frequently demonstrated the inadequate knowledge and abilities of the employees in enterprises, according to numerous reports and research. Organizations should prioritise and make significant investments in training and certifications to address this issue, ensuring that the workforce has the necessary understanding of MLOps models and strategies and can put those tactics into practise for effective data management.

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Global MLOps Market Competitive Landscape

The global MLOps market is characterized by a mix of established companies and emerging players. Market participants are focusing on research and development activities to enhance the efficiency and performance of MLOpss. Additionally, strategic collaborations, partnerships, and mergers and acquisitions are prevalent strategies adopted by companies to expand their market presence. The competitive environment is further influenced by factors such as technological advancements, government regulations, and the ability to provide cost-effective and sustainable solutions.

MLOps Market Top Player’s Company Profiles

  • Google (US)
  • HE (US)
  • GAVS Technologies (US)
  • DataRobot (US)
  • Cloudera (US)
  • Altery (US)
  • Domino Data Lab (US)
  • Valohai (US)
  • H20.ai (US)
  • MLflow (Netherlands)
  • Neptune. ai (Europe)
  • Comet (US)
  • SparkCognition (US)
  • Hopsworks (Europe)
  • Datatron (US)
  • Weights & Biases (US)
  • Katonic.ai (Australia)

MLOps Market Recent Developments

  • In August 2021, Kubeflow released version 1.4, featuring improvements for deploying, using, and managing machine learning workflows and pipelines on Kubernetes.

Global MLOps Key Market Trends

  • Few key market trends in the global MLOps market are MLOps platforms incorporating more automation features, such as auto-model selection and auto-tuning. This is helping businesses to reduce the time and cost of model development. Many MLOps platforms are cloud-based, as they offer scalability and flexibility to businesses. Cloud providers are also offering more machine learning services, such as automatic model deployment and monitoring. Collaboration between data scientists and IT operations teams is key to effective MLOps. Platforms are incorporating collaboration features to facilitate this. As machine learning models become more complex, it is becoming increasingly important to understand how they make decisions. Explainable AI is a trend in MLOps that aims to provide insight into why machine learning models make certain decisions. As more sensitive data is being used in machine learning models, ensuring security is crucial. MLOps platforms are incorporating security features to protect sensitive data and prevent cyber attacks.

Global MLOps Market SkyQuest Analysis

SkyQuest’s ABIRAW (Advanced Business Intelligence, Research & Analysis Wing) is our Business Information Services team that Collects, Collates, Co-relates, and Analyses the Data collected by means of Primary Exploratory Research backed by robust Secondary Desk research.

MLOps, or machine learning operations, is the process of managing the entire lifecycle of machine learning models. This includes everything from development and training to deployment and monitoring. MLOps ensures that models are accurate, reliable, and scalable. It involves a combination of software engineering, data science, and operations management. MLOps is becoming increasingly important as machine learning is being used more widely in business applications. Effective MLOps can help organizations to improve their decision-making processes, increase efficiency, and reduce costs.

Report Metric Details
Market size value in 2022 USD 1.10 billion
Market size value in 2031 USD 24.23 billion
Growth Rate 41%
Base year 2023
Forecast period 2024-2031
Forecast Unit (Value) USD Billion
Segments covered
  • Infrastructure
    • Data Infrastructure and Model Infrastructure
  • Data Management
    • Data Pipeline Management and Data Versioning
Regions covered North America (US, Canada), Europe (Germany, France, United Kingdom, Italy, Spain, Rest of Europe), Asia Pacific (China, India, Japan, Rest of Asia-Pacific), Latin America (Brazil, Rest of Latin America), Middle East & Africa (South Africa, GCC Countries, Rest of MEA)
Companies covered
  • Google (US)
  • HE (US)
  • GAVS Technologies (US)
  • DataRobot (US)
  • Cloudera (US)
  • Altery (US)
  • Domino Data Lab (US)
  • Valohai (US)
  • H20.ai (US)
  • MLflow (Netherlands)
  • Neptune. ai (Europe)
  • Comet (US)
  • SparkCognition (US)
  • Hopsworks (Europe)
  • Datatron (US)
  • Weights & Biases (US)
  • Katonic.ai (Australia)
Customization scope

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Table Of Content

Executive Summary

Market overview

  • Exhibit: Executive Summary – Chart on Market Overview
  • Exhibit: Executive Summary – Data Table on Market Overview
  • Exhibit: Executive Summary – Chart on Global MLOps Market Characteristics
  • Exhibit: Executive Summary – Chart on Market by Geography
  • Exhibit: Executive Summary – Chart on Market Segmentation
  • Exhibit: Executive Summary – Chart on Incremental Growth
  • Exhibit: Executive Summary – Data Table on Incremental Growth
  • Exhibit: Executive Summary – Chart on Vendor Market Positioning

Parent Market Analysis

Market overview

Market size

  • Market Dynamics
    • Exhibit: Impact analysis of DROC, 2021
      • Drivers
      • Opportunities
      • Restraints
      • Challenges
  • SWOT Analysis

KEY MARKET INSIGHTS

  • Technology Analysis
    • (Exhibit: Data Table: Name of technology and details)
  • Pricing Analysis
    • (Exhibit: Data Table: Name of technology and pricing details)
  • Supply Chain Analysis
    • (Exhibit: Detailed Supply Chain Presentation)
  • Value Chain Analysis
    • (Exhibit: Detailed Value Chain Presentation)
  • Ecosystem Of the Market
    • Exhibit: Parent Market Ecosystem Market Analysis
    • Exhibit: Market Characteristics of Parent Market
  • IP Analysis
    • (Exhibit: Data Table: Name of product/technology, patents filed, inventor/company name, acquiring firm)
  • Trade Analysis
    • (Exhibit: Data Table: Import and Export data details)
  • Startup Analysis
    • (Exhibit: Data Table: Emerging startups details)
  • Raw Material Analysis
    • (Exhibit: Data Table: Mapping of key raw materials)
  • Innovation Matrix
    • (Exhibit: Positioning Matrix: Mapping of new and existing technologies)
  • Pipeline product Analysis
    • (Exhibit: Data Table: Name of companies and pipeline products, regional mapping)
  • Macroeconomic Indicators

COVID IMPACT

  • Introduction
  • Impact On Economy—scenario Assessment
    • Exhibit: Data on GDP - Year-over-year growth 2016-2022 (%)
  • Revised Market Size
    • Exhibit: Data Table on Global MLOps Market size and forecast 2021-2027 ($ million)
  • Impact Of COVID On Key Segments
    • Exhibit: Data Table on Segment Market size and forecast 2021-2027 ($ million)
  • COVID Strategies By Company
    • Exhibit: Analysis on key strategies adopted by companies

MARKET DYNAMICS & OUTLOOK

  • Market Dynamics
    • Exhibit: Impact analysis of DROC, 2021
      • Drivers
      • Opportunities
      • Restraints
      • Challenges
  • Regulatory Landscape
    • Exhibit: Data Table on regulation from different region
  • SWOT Analysis
  • Porters Analysis
    • Competitive rivalry
      • Exhibit: Competitive rivalry Impact of key factors, 2021
    • Threat of substitute products
      • Exhibit: Threat of Substitute Products Impact of key factors, 2021
    • Bargaining power of buyers
      • Exhibit: buyers bargaining power Impact of key factors, 2021
    • Threat of new entrants
      • Exhibit: Threat of new entrants Impact of key factors, 2021
    • Bargaining power of suppliers
      • Exhibit: Threat of suppliers bargaining power Impact of key factors, 2021
  • Skyquest special insights on future disruptions
    • Political Impact
    • Economic impact
    • Social Impact
    • Technical Impact
    • Environmental Impact
    • Legal Impact

Market Size by Region

  • Chart on Market share by geography 2021-2027 (%)
  • Data Table on Market share by geography 2021-2027(%)
  • North America
    • Chart on Market share by country 2021-2027 (%)
    • Data Table on Market share by country 2021-2027(%)
    • USA
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • Canada
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
  • Europe
    • Chart on Market share by country 2021-2027 (%)
    • Data Table on Market share by country 2021-2027(%)
    • Germany
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • Spain
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • France
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • UK
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • Rest of Europe
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
  • Asia Pacific
    • Chart on Market share by country 2021-2027 (%)
    • Data Table on Market share by country 2021-2027(%)
    • China
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • India
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • Japan
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • South Korea
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • Rest of Asia Pacific
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
  • Latin America
    • Chart on Market share by country 2021-2027 (%)
    • Data Table on Market share by country 2021-2027(%)
    • Brazil
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • Rest of South America
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
  • Middle East & Africa (MEA)
    • Chart on Market share by country 2021-2027 (%)
    • Data Table on Market share by country 2021-2027(%)
    • GCC Countries
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • South Africa
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • Rest of MEA
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)

KEY COMPANY PROFILES

  • Competitive Landscape
    • Total number of companies covered
      • Exhibit: companies covered in the report, 2021
    • Top companies market positioning
      • Exhibit: company positioning matrix, 2021
    • Top companies market Share
      • Exhibit: Pie chart analysis on company market share, 2021(%)

Methodology

For the Global MLOps Market, our research methodology involved a mixture of primary and secondary data sources. Key steps involved in the research process are listed below:

1. Information Procurement: This stage involved the procurement of Market data or related information via primary and secondary sources. The various secondary sources used included various company websites, annual reports, trade databases, and paid databases such as Hoover's, Bloomberg Business, Factiva, and Avention. Our team did 45 primary interactions Globally which included several stakeholders such as manufacturers, customers, key opinion leaders, etc. Overall, information procurement was one of the most extensive stages in our research process.

2. Information Analysis: This step involved triangulation of data through bottom-up and top-down approaches to estimate and validate the total size and future estimate of the Global MLOps Market.

3. Report Formulation: The final step entailed the placement of data points in appropriate Market spaces in an attempt to deduce viable conclusions.

4. Validation & Publishing: Validation is the most important step in the process. Validation & re-validation via an intricately designed process helped us finalize data points to be used for final calculations. The final Market estimates and forecasts were then aligned and sent to our panel of industry experts for validation of data. Once the validation was done the report was sent to our Quality Assurance team to ensure adherence to style guides, consistency & design.

Analyst Support

Customization Options

With the given market data, our dedicated team of analysts can offer you the following customization options are available for the Global MLOps Market:

Product Analysis: Product matrix, which offers a detailed comparison of the product portfolio of companies.

Regional Analysis: Further analysis of the Global MLOps Market for additional countries.

Competitive Analysis: Detailed analysis and profiling of additional Market players & comparative analysis of competitive products.

Go to Market Strategy: Find the high-growth channels to invest your marketing efforts and increase your customer base.

Innovation Mapping: Identify racial solutions and innovation, connected to deep ecosystems of innovators, start-ups, academics, and strategic partners.

Category Intelligence: Customized intelligence that is relevant to their supply Markets will enable them to make smarter sourcing decisions and improve their category management.

Public Company Transcript Analysis: To improve the investment performance by generating new alpha and making better-informed decisions.

Social Media Listening: To analyze the conversations and trends happening not just around your brand, but around your industry as a whole, and use those insights to make better Marketing decisions.

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FAQs

Global MLOps Market size was valued at USD 1.10 billion in 2022 and is poised to grow from USD 1.55 billion in 2023 to USD 24.23 billion by 2031, growing at a CAGR of 41% during the forecast period (2024-2031).

The global MLOps market is characterized by a mix of established companies and emerging players. Market participants are focusing on research and development activities to enhance the efficiency and performance of MLOpss. Additionally, strategic collaborations, partnerships, and mergers and acquisitions are prevalent strategies adopted by companies to expand their market presence. The competitive environment is further influenced by factors such as technological advancements, government regulations, and the ability to provide cost-effective and sustainable solutions. 'IBM (US)', 'Microsoft (US)', 'Google (US)', 'AWS (US)', 'HE (US)', 'GAVS Technologies (US)', 'DataRobot (US)', 'Cloudera (US)', 'Altery (US)', 'Domino Data Lab (US)', 'Valohai (US)', 'H20.ai (US)', 'MLflow (Netherlands)', 'Neptune. ai (Europe)', 'Comet (US)', 'SparkCognition (US)', 'Hopsworks (Europe)', 'Datatron (US)', 'Weights & Biases (US)', 'Katonic.ai (Australia)'

Manual data reprocessing and collecting are ineffective and might produce unsatisfactory results. MLOps helps for automating the whole ML model workflow. This includes data gathering, model construction, testing, retraining, and development. MLOps help companies save time and reduce error rates. Collaboration is seen between IT and business personnel, as well as data scientists and engineers, for the company-wide adoption of ML models. Businesses can standardize ML operations and establish a standardized language for all participants due to MLOps principles. This reduces compatibility problems and quickens the construction and deployment of modelling processes.

Few key market trends in the global MLOps market are MLOps platforms incorporating more automation features, such as auto-model selection and auto-tuning. This is helping businesses to reduce the time and cost of model development. Many MLOps platforms are cloud-based, as they offer scalability and flexibility to businesses. Cloud providers are also offering more machine learning services, such as automatic model deployment and monitoring. Collaboration between data scientists and IT operations teams is key to effective MLOps. Platforms are incorporating collaboration features to facilitate this. As machine learning models become more complex, it is becoming increasingly important to understand how they make decisions. Explainable AI is a trend in MLOps that aims to provide insight into why machine learning models make certain decisions. As more sensitive data is being used in machine learning models, ensuring security is crucial. MLOps platforms are incorporating security features to protect sensitive data and prevent cyber attacks.

North America followed by Asia Pacific is one of the leading markers for MLOps in terms of market share. Countries, such as the US and Canada, are adopting ML technology in multiple application areas, propelling the growth of MLOps in this region. In the North American MLOps market, the US is considered one of the major contributors. The presence of prominent technology providers, such as IBM (US), Google (US), Microsoft (US), HPE (US), and AWS (US), is complementing the growth of the market in this region. The presence of such established MLOps companies and the emergence of new start-ups will strengthen the outlook of this region and enable it to witness a significant increase in investments and early adoption of Artificial Intelligence technology.

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