MLOps Market 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 Overview

As per Maximize Market Research, a global business research and consultancy firm, the total global market for MLOps Market was USD in 2022 and is expected to grow at a CAGR of percent over the forecast period to reach USD by 2029.

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MLOps Market Scope and Methodology

The bottom-up approach was used in the report to estimate the MLOps Market.  The primary and secondary data sources were used to collect data. The primary source involved interviews with significant market leaders and industry experts including business owners and marketing specialists while secondary sources are a combination of the reviews of the financial and annual reports of top manufacturers. For better information on market penetration, competitive landscape demand analysis, and regional analysis are taken at the local, regional, and global levels. The report presented the PORTER and PESTLE Analysis with the potential impact of micro-economic factors of the MLOps market. SWOT analysis is used to identify the threats and weaknesses of the market while PORTER is used to determine the competitive intensity of the industry in the MLOps market. 

MLOps Market Regional Insights

Categorized broadly, the MLOps market is segmented across North America, Europe, Asia Pacific, Latin America, the Middle East, and Africa. The report comprehensively analyzes factors, market size, growth rate, and regional import-export activities. The regional analysis extends to both local and country levels for a comprehensive examination.

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.

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)
• BFSI
• 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

 

 

 

 

 

 

 

 

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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
17. Cnvrg.io

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Key questions answered in the MLOps Market are:

  • What is MLOps?
  • What was the MLOps market size in 2022?
  • What are the different segments of the MLOps Market?
  • What growth strategies are the players considering to increase their presence in MLOps?
  • What are the upcoming industry applications and trends for the MLOps Market?
  • What are the recent industry trends that can be implemented to generate additional revenue streams for the MLOps Market?
  • Who are the key players in the MLOps market?
  • What segments are covered in the MLOps Market?
  • Who are the leading companies and what are their portfolios in MLOps Market? 

Key Offerings:

  • Past Market Size and Competitive Landscape (2018 to 2021)
  • Past Pricing and price curve by region (2018 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 by segment with their sub-segments and Region 
  • 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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