MLOps Market with Top Countries Data, Industry Insights by Top Key Players, Types and Applications


MLOps Market Report size was valued at USD 1.06 Bn in 2022 and is expected to reach USD 22.1 Bn by 2029, at a CAGR of 38.7%

MLOps Market Report Overview

A new report published by Global Market Intelligence firm, Maximize Market Research, indicates that the MLOps Market is expected to grow significantly in the coming years. The report identifies the key drivers of growth, as well as the major restraints and challenges that the market is facing. It also provides insights into the potential opportunities that exist in the market.

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MLOps Market Report Scope

The report provides current MLOps market trends and regional insights, including demand, supply, and sales, as well as recent changes in the market. It focuses on the key drivers and restraints for key players, as well as the current competitive landscape. The report also provides the latest data on market statistics, future developments, size, and emerging trends, which can help you identify the products and end users that are driving revenue growth and profitability. It also includes company profiles, product specifications, production capacity/sales, revenue, price, gross margin, and sales by product.

MLOps Market Regional Analysis

The MLOps market report provides a comprehensive overview of the market, including its geographic condition, market size and share, business network structure, opportunities, and news updates.

MLOps Market Segmentation

Deployment Mode: MLOps platforms can be set up in the cloud or on-premises. On-premises deployment entails setting up MLOps infrastructure and software on the client’s property. As the software is licensed and the complete instance of the software resides on-site, on-premise software necessitates that an enterprise obtain a license or a copy of the software in order to utilize it. On the other hand, cloud-based deployment involves hosting and managing the MLOps platform using cloud infrastructure and services. A cloud-based server makes use of virtual technologies to host programs remotely for businesses. Data is routinely backed up, there are no capital expenses, and businesses only pay for the services they consume.

Organization Size: From small and medium-sized businesses (SMEs) to major corporations, the ML Ops market serves organizations of all sizes. Larger businesses often have more complex ML workflows and need more robust ML Ops solutions, whereas SMEs typically have lesser IT budgets and demand more economical ML Ops solutions.

Industry Vertical: A wide number of industry verticals, including BFSI, healthcare, retail, telecommunications, and others, are served by the ML Ops market. diverse industry verticals have diverse ML use cases and requirements, such as banks using MLOps to expand ML models, reduce operational costs, and address pressing issues with data management like ethics, accountability, and transparency. Multi-talented teams can collaborate more effectively and complete more tasks in a standardized manner thanks to MLOps. And ML Ops platforms must be customized to satisfy these particular requirements.

The many parts of an ML Ops platform, such as model deployment, model training, model management, data management, and monitoring and governance, are used to segment the ML Ops market. ML pipelines have been automated by many businesses, yet models are still not compliant with regulatory standards. A 2021 Algorithmia-Study found that 56% of participants ranked implementing model governance as one of the most difficult aspects of successfully putting ML apps into production. Decrease in deployment time, increased scalability, and decreased error percentages are the three key MLOps components that are required.

Application: Based on ML Ops’ specialized applications, like fraud detection, predictive maintenance, recommendation engines, and others, the market for ML Ops is also divided into different submarkets. Fraud detection has been done via MLOps, where ML models are taught to spot fraudulent activities in real time. This sample application makes use of MLOps techniques to help identify phony insurance and credit card transactions. The development and application of necessary machine learning models, such as those that forecast patient outcomes or identify potential health issues before they become serious, is made simpler by MLOps in the lucrative and crucial field of predictive healthcare.

1 MLOps Market, by Deployment Mode (2022-2029)
• On-Premises deployment
o Installation of MLOps software
o Infrastructure on the customer’s premises
• Cloud-Based Deployment
o Use of Cloud Infrastructure
o Services to host and manage the MLOps platform
2 MLOps Market, by Organization Size (2022-2029)
• Small & Medium-Sized Enterprises
• Large enterprises
3 MLOps Market, by Industry Vertical (2022-2029)
• Healthcare
• Retail
• Telecommunication
• Others
4 MLOps Market, by Component (2022-2029)
• Model Deployment
• Model Training
• Model Management
• Data Management
• Monitoring and Governance
5 MLOps Market, by Application (2022-2029)
• Fraud Detection
• Predictive Maintenance
• Recommendation Engines
• Others

For any Queries Linked with the Report, Ask an Analyst 

MLOps Market Key Players

• Microsoft
1. Amazon
2. Google
3. IBM
4. Dataiku
5. Lguazio
6. Databricks
7. DataRobot, Inc.
8. Cloudera
9. Modzy
10. Algorithmia
11. HPE
12. Valohai
13. Allegro AI
14. Comet
15. FloydHub
16. Paperpace

For any Queries Linked with the Report, Ask an Analyst 

Key Questions answered in the MLOps Market Report are:


  • What is MLOps?
  • What is the forecast period of the MLOps Market?
  • What is the expected MLOps market size by the end of the forecast period?


  • What will be the CAGR of the MLOps market during the forecast period?
  • Who are the key players in the MLOps industry?
  • Which region held the largest market share in the MLOps Market?
  • What are the opportunities for the MLOps Market?
  • What are the factors restraining the MLOps market growth?

Key Offerings:

  • Past Market Size and Competitive Landscape (2017 to 2021)
  • Past Pricing and price curve by region (2017 to 2021)
  • Market Size, Share, Size & Forecast by different segment | 2023−2029
  • Market Dynamics – Growth Drivers, Restraints, Opportunities, and Key Trends by Region
  • Market Segmentation – A detailed analysis of segments and their sub-segments
  • Competitive Landscape – Profiles of selected key players by region from a strategic perspective 
    • Competitive landscape – Market Leaders, Market Followers, Regional player
    • Competitive benchmarking of key players by region
  • PESTLE Analysis 
  • PORTER’s analysis 
  • Value chain and supply chain analysis 
  • Legal Aspects of Business by Region
  • Lucrative business opportunities with SWOT analysis 
  • Recommendations 

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