The Global Machine Learning Model Operationalization Management (MLOps) Market size is expected to reach $8.5 billion by 2028, rising at a market growth of 38.9% CAGR during the forecast period.
Data scientists and operations specialists can work together and communicate using MLOps, a set of techniques. Implementing Machine Learning and Deep Learning models in large production environments can be automated while improving quality and streamlining the management process. In addition, aligning models with business demands and regulatory standards is simpler.
MLOps is gradually becoming a stand-alone method for managing the ML lifecycle. It covers every lifecycle stage, including data collection, model building (using the software development lifecycle and continuous integration/delivery), deployment, orchestration, health, governance, diagnostics, and business metrics.
Machine learning technology solutions are being aggressively adopted by businesses to improve the customer experience and support maximizing profit. Market participants are implementing advanced data processing and integration strategies to gather insights and get a competitive edge over rivals. The use of MLOps in enterprises is still in its infancy.
As people become more aware of the advantages of doing so, there will likely be lucrative chances for market expansion. The demand for cutting-edge solutions for improved data management is fueled by the expanding usage of data science technologies for improvements in computing power, artificial intelligence, and system learning.
The well-known industry verticals, such as retail, healthcare, education, telecommunication, manufacturing, and financial institutions, have a significant demand for machine learning. Standardized models and workflows are made possible with the assistance of ML Ops. Additionally, it facilitates the simple implementation of machine learning technology anywhere, which is the primary factor in enterprises' high preference for them.
The COVID-19 pandemic is anticipated to be aided by artificial intelligence technology. Several nations are using population surveillance techniques made possible by machine learning and artificial intelligence to track and trace COVID-19 cases. For instance, researchers in South Korea use geo-location information and surveillance camera footage to monitor coronavirus cases. In addition, data scientists use machine intelligence algorithms to anticipate the location of the next outbreak and notify the appropriate authorities, allowing for real-time illness tracking. This has permitted technologically advanced nations to put a speed breaker on the spread of the virus. Such active endeavors are projected to increase the demand for machine intelligence solutions during the upcoming period.
Manual data collection and reprocessing are inefficient and may yield unacceptable results. MLOps aids in automating the entire workflow of ML models. This comprises data collection, the model creation, testing, retraining, and deployment. MLOps assist businesses in reducing errors and saving time. For the company-wide adoption of ML models, IT and business professionals and data scientists and engineers are involved in cooperation.
Financial institutions possess a vast amount of client information. They may collect information on purchases, spending habits, platform usage, and geo-locational preferences in addition to standard banking information, such as bank account balances, to create a 360-degree image of the consumer. This enables the bank to offer goods and services that are particularly tailored to the customer's requirements and preferences. Therefore, the growing use of ML in the financial industry will fuel the expansion of the MLOps market.
While more SMBs in the machine learning as a service industry use cloud-based services, the time-consuming machine learning integration process will become significantly less time-consuming. It helps to enhance an organization's efficiency without recruiting human resources by avoiding repetitive work. Organizations need now utilise MLOps in data management to collect and integrate the enormous volumes of data from several internal and external data sources and unite the data silos.
Based on components, the Machine Learning Model Operationalization Management (MLOps) Market is categorized into Platform and Services. In 2021, the services segment recorded a sizable revenue share. MLOps solutions are being adopted by businesses worldwide to strengthen their customer interaction, brand recognition, and marketing initiatives. Organizations can effortlessly engage consumers, communicate more effectively, and broaden their reach using MLOps marketing tools.
Based on deployment mode, the Machine Learning Model Operationalization Management (MLOps) Market is classified into On-Premises and Cloud. The cloud category had the most revenue share in the market in 2021. To boost employee productivity, the cloud-based system enables worldwide IT task outsourcing. Three other types of cloud computing exist private, public, and hybrid. The public cloud's rising popularity is primarily due to its numerous organizational advantages, including flexibility and scalability, remote access, simplicity, speedier installation, and many other benefits.
Based on organization size, the Machine Learning Model Operationalization Management (MLOps) Market is categorized into Large Enterprises and SMEs based on Organization Size. In 2021, the small and medium-sized business segment obtained a sizeable revenue share. This is because machine learning adoption enables SMEs to optimize their processes on a limited budget. Shortly, it is anticipated that AI and machine learning will be the key technologies that let SMEs access digital resources and save money on ICT.
Based on vertical, the Machine Learning Model Operationalization Management (MLOps) Market is categorized into BFSI, Retail and eCommerce, Government and Defense, Healthcare and Life Sciences, Manufacturing, Telecom, IT and ITeS, Energy, and Utilities, Transportation and Logistics, and Others. The BFSI sector produced the highest revenue share in the market in 2021. However, most banks also experience considerable difficulties managing inert models, particularly in settings where application deployments could be more active and influential. MLOps, which essentially applies DevOps techniques and methods to machine learning, can assist banks in swiftly and effectively addressing some of these issues.
| Report Attribute | Details |
|---|---|
| Market size value in 2021 | USD 871.3 Million |
| Market size forecast in 2028 | USD 8.5 Billion |
| Base Year | 2021 |
| Historical Period | 2018 to 2020 |
| Forecast Period | 2022 to 2028 |
| Revenue Growth Rate | CAGR of 38.9% from 2022 to 2028 |
| Number of Pages | 309 |
| Number of Tables | 483 |
| Report coverage | Market Trends, Revenue Estimation and Forecast, Segmentation Analysis, Regional and Country Breakdown, Competitive Landscape, Companies Strategic Developments, Company Profiling |
| Segments covered | Component, Organization size, Deployment Mode, Vertical, Region |
| Country scope | US, Canada, Mexico, Germany, UK, France, Russia, Spain, Italy, China, Japan, India, South Korea, Singapore, Malaysia, Brazil, Argentina, UAE, Saudi Arabia, South Africa, Nigeria |
| Growth Drivers |
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| Restraints |
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Based on geography, the Machine Learning Model Operationalization Management (MLOps) Market is classified into North America, Europe, Asia Pacific, and LAMEA. North America is anticipated to hold the most significant market share during the projection period. By market share, North America is one of the top regions for MLOps. MLOps in this region are expanding due to the use of ML technology by nations like the US and Canada in various application fields. The US is regarded as one of the key contributors to North American MLOps market.
Free Valuable Insights: Global Machine Learning Model Operationalization Management (MLOps) Market size to reach USD 8.5 Billion by 2028
The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation and Google LLC are the forerunners in the Machine Learning Model Operationalization Management (MLOps). Companies such as Amazon Web Services, Inc. (Amazon.com, Inc.), IBM Corporation, Hewlett-Packard enterprise Company are some of the key innovators in Machine Learning Model Operationalization Management (MLOps).
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Microsoft Corporation, Amazon Web Services, Inc. (Amazon.com, Inc.), Google LLC, IBM Corporation, Hewlett-Packard enterprise Company, Alteryx, Inc., Cloudera, Inc., DataRobot, Inc., Domino Data Lab, Inc., and H2O.ai, Inc.
By Component
By Vertical
By Organization size
By Deployment Mode
By Geography
The global Machine Learning Model Operationalization Management (MLOps) Market size is expected to reach $8.5 billion by 2028.
ML should be standardized for efficient teamwork are driving the market in coming years, however, Lack of Expertise restraints the growth of the market.
Microsoft Corporation, Amazon Web Services, Inc. (Amazon.com, Inc.), Google LLC, IBM Corporation, Hewlett-Packard enterprise Company, Alteryx, Inc., Cloudera, Inc., DataRobot, Inc., Domino Data Lab, Inc., and H2O.ai, Inc.
The Platform segment acquired maximum revenue share in the Global Machine Learning Model Operationalization Management (MLOps) Market by Component in 2021 thereby, achieving a market value of $6.5 billion by 2028.
The Large Enterprises segment is leading the Global Machine Learning Model Operationalization Management (MLOps) Market by Organization size in 2021 thereby, achieving a market value of $5.6 billion by 2028.
The North America market dominated the Global Machine Learning Model Operationalization Management (MLOps) Market by Region in 2021, and would continue to be a dominant market till 2028; thereby, achieving a market value of $3.3 billion by 2028, growing at a CAGR of 37.5 % during the forecast period.
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