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The global ModelOps market was valued at USD 4 billion in 2023 and grew at a CAGR of 36% from 2024 to 2033. The market is expected to reach USD 86.58 billion by 2033. The rising automation and digitization globally will drive the growth of the global ModelOps market.
ModelOps or Model Operations are business practices that define how AI and machine learning models are created, deployed, managed and used in an organization. It also encompasses all the steps of modelling right from conceptual modelling, logical modelling right up to physical modelling for the models to be deployed in the actual systems. The scope of ModelOps ranges widely to cover all types of AI models, including rule-based systems and decision models. ModelOps is designed for the management of the procedures associated with deployment and usage of the models in order to enhance the performance of their functions. A typical ModelOps pipeline includes several critical stages including model development which is about creating and training models. Next is model validation, where the models go through several tests to ensure that the predictions they make are accurate, the outcomes are fair for all the respondents and whether they meet regulatory requirements. Model deployment, where the models are run in live environment and model monitoring which is the process of verifying the model’s performance. Another significant component is Model governance which primarily captures the considerations of the legal aspect and the ethical considerations of using models in sensitive parts of health and economy sectors. ModelOps enables companies to respond to the issues that organisations face when implementing AI enhancements, arming organisations with the tools to support a feedback loop, improve models based on their performance, changing regulations etc.
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The rising integration of AI and ML across sectors – Since organizations are integrating AI into many departments, the number of models being deployed will also increase. The next models are used in various capacities such as prediction, process control, competitive differentiation, and decision-making processes of the companies’ financial functioning, hospitals, manufacturing industries, retail stores, and many other areas. However, there are specific challenges when it comes to moving to the next level with AI projects. Earlier, the handling of models using specific control mechanisms was not very effective in handling the complexities that are associated with the present world and the corresponding demands of a large number of models. This is where ModelOps as a concept comes into play. It enhances the management, deployment, and monitoring of AI models within firms. It also helps the performance of model monitoring on a regular basis. In conclusion, ModelOps prepares an organization to achieve the maximum value of an AI investment and with the growing adoption and integration of AI, the market for ModelOps is bound to grow and develop in the coming years.
The high implementation costs of ModelOps – One of the biggest challenges that affect ModelOps deployment is the high implementation costs. Building out a ModelOps suite requires both a significant technology investment as well as a commitment of time and resources that can be difficult for small and mid-size organizations. The costs start with powerful foundation that specifically implies certain infrastructural structures. ModelOps involves high performing computer processing which may need cloud computing or top-tier on-site infrastructure deployment for model deployment, surveillance, or retraining. The implementation of this kind of infrastructure can already be expensive on its own, especially for organizations that may not necessarily be at a very advanced stage of AI or IT integration. Aside from the necessary IT infrastructure, it is required to have focused software applications to handle the model’s life cycle. Such tools can be costly and are sometimes paid per license. Moreover, ModelOps needs skill and knowledge in different areas. Coordination of this complicated framework incurs an added cost because professionals who can reliably coordinate the project and secure funding, personnel, and partnerships are rare and costly.
Regulatory environment pushes for the adoption of ModelOps – Legal and ethical requirements for model deployment serve as major reasons to adopt ModelOps, especially in financial, healthcare, insurance, and government businesses. Governance relates to guidelines that one has to follow when implementing artificial intelligence models. Often industries are mandated to adhere by certain set of rules or guidelines depending on the sector of the economy. Noncompliance is punishable by law and may lead to massive lawsuits, tarnishing of the organization’s brand image, and causing the client to lose confidence in an organization. Consequently, ModelOps has an essential function of enabling governance frameworks that govern them across their life cycles. ModelOps make it easier for an organisation to maintain the accountability. ModelOps also helps to check models frequently for fairness, bias, and adherence to norms and values of both legal and corporate governance.
The regions analysed for the market include North America, Europe, South America, Asia Pacific, the Middle East, and Africa. North America emerged as the most significant global ModelOps market, with a 41% market revenue share in 2023.
North America is home to a vast number of technology companies, start-ups, and research centres that spend a lot of capital on AI. The strong background of renowned financial services, healthcare, and technology organizations, which chiefly use AI contribute to the increasing adoption of ModelOps in these sectors. Such industries dedicate significant resources to AI and machine learning projects, meaning these models need robust governance structures to adhere to industry guidelines, and enhance business outputs. The BFSI sector has widely incorporated ModelOps to address immensely strict regulatory standards and enhance risk calculation via real-time analysis. Additionally, cloud adoption is high in the region together with advanced computing technologies that are necessary for efficient model deployment and management. Furthermore, the regulation governing application of AI in North America is more liberal to support innovation which also augments the market’s growth.
North America Region ModelOps Market Share in 2023 - 41%
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The deployment type segment is divided into cloud and on-premises. The cloud segment dominated the market, with a market share of around 58% in 2023. The most popular ModelOps deployment model is the cloud model. Cloud solutions offer flexibility or the ability to scale up or down depending on the demands and workload. This flexibility is important as different organizations go through fluctuations in the amount of work required by computational models. Cloud solutions are efficient and cost-effective. Paying only for what you use implies that organizations do not have to make the bigger investments unlike in the case of on-premises deployment model. In addition, cloud environments supplement flexibility, and user can easily share data and ideas since they can work remotely. This is even more crucial as companies continue with remote and a hybrid working model. Furthermore, cloud service providers are also required to manage and handle automatic update and maintenance of the system where the organizations always get the new features and latest secured modes without enhancing extra operational workload.
The application segment is divided into CI/CD (continuous integration/ continuous deployment), model lifecycle management, dashboard and reporting, governance and compliance, monitoring and alerting, and others. The monitoring and alerting segment dominated the market, with a market share of around 36% in 2023. Monitoring and Alerting remains the most common use case for ModelOps as it is critical for maintaining machine learning models at scale. Since organizations use AI solutions at large volumes, persistent model checking becomes crucial in identifying cases of data drift, model decay, and performance reduction. Monitoring embraces parameters of evaluation like the accuracy of the predicted outcome, time taken to respond and the utilization of resources. The proactive method aids in keeping the model dependable, and so the decision-making relying on such models is sound and efficient. Besides, monitoring and alerting help in addressing compliance and governance to enhance contractual compliance and also across regulated sectors. The monitoring can also give some insights for model retraining and optimizing for operations when done effectively. This knowledge allows organizations to enhance their models for own application so that they correspond to the conditions in which they are and perform effectively in various conditions possible over time.
The end user segment is divided into IT and telecom, BFSI, healthcare, manufacturing, retail and ecommerce, government and defence, and others. The BFSI segment dominated the market, with a market share of around 33% in 2023. The largest segment in the ModelOps market is BFSI due to its critical reliance on data insights to make decisions and the rising challenge of compliance that has contributed towards the need for ModelOps. Many applications of AI as well as machine learning models are used by financial institutions ranging from risk evaluation, fraud detection, credit rating, algorithms for trading, and automated customer relationship management. Since the sector is data intensive and requires proper analysis of data, proper management of the models assumed becomes important. In the case of BFSI companies, particularly, constant monitoring, as well as, reinvention is a key requirement due to business implications. There is still high pressure from the regulatory authorities on transparency, accountability, and risk management that means that the financial industries have to be assured that the models they have estimated will function properly and will not violate the legal requirements. This can be done through using ModelOps which offers reference frameworks on how to track model performance, how to notice that there is a change in desired performance standards, and how to act on those deviations. Apart from risk management this capability supports customer trust and management of regulatory compliance.
| Attribute | Description |
|---|---|
| Market Size | Revenue (USD Billion) |
| Market size value in 2023 | USD 4 Billion |
| Market size value in 2033 | USD 86.58 Billion |
| CAGR (2024 to 2033) | 36% |
| Historical data | 2020-2022 |
| Base Year | 2023 |
| Forecast | 2024-2033 |
| Region | The regions analysed for the market are Asia Pacific, Europe, South America, North America, and Middle East and Africa. Furthermore, the regions are further analysed at the country level. |
| Segments | Deployment Type, Application, and End User |
As per The Brainy Insights, the size of the global ModelOps market was valued at USD 4 billion in 2023 to USD 86.58 billion by 2033.
Global ModelOps market is growing at a CAGR of 36% during the forecast period 2024-2033.
The market's growth will be influenced by the rising integration of AI and ML across sectors.
The high implementation costs of ModelOps could hamper the market growth.
This study forecasts revenue at global, regional, and country levels from 2020 to 2033. The Brainy Insights has segmented the global ModelOps market based on below mentioned segments:
Global ModelOps Market by Deployment Type:
Global ModelOps Market by Application:
Global ModelOps Market by End User:
Global ModelOps Market by Region:
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