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.
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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