Machine Learning Model Operationalization Management (MLOPS) Market - Forecasts from 2023 to 2028

Machine learning model operationalization management (MLOPS) market is expected to grow at a CAGR of 47.81% from a market size of US$842.105 million in 2021 to reach US$12,981.316 million in 2028.

Machine learning model operationalization management (MLOps) is the process of managing and deploying machine learning models in a business environment and involves the integration of data pipelines, model training, deployment, monitoring, and optimization to ensure the effective functioning of ML models in production environments. The adoption of MLOps is essential to companies as it ensures that the development of models by data scientists can generate accurate predictions and recommendations in real-world scenarios. In addition, they help enterprises to optimize their models and ensure their effectiveness over time as the underlying data changes. The increasing complexity of ML is stimulating the demand for MLOps solutions they provide a framework for managing the entire lifecycle of machine learning models, from development to deployment and beyond, and lower the management difficulties for companies. Further, the increasing Importance of data privacy and security is contributing to the growth of the MLOps market as companies need to ensure that their machine learning models are secure and compliant with regulations.

Market Drivers

Increasing adoption rate of machine learning

The increasing adoption of machine learning by enterprises to improve their operations and gain competitive advantage is creating a high demand for MLOps to deploy ML models at scale. The state of process automation report generated in 2020 by Camunda, a software company projected that more than 84% of the companies are planning to invest an additional amount to facilitate process automation. The enormous growth in the volume of data produced and assembled by companies is driving up the demand for ML data analysis and prediction models to assist businesses in processing the data quickly, accurately, and efficiently and further derive valuable insights from the data. For example, the PayPal corporation revealed that by employing the AutoML technology from H2O.ai, the predictive accuracy of its fraud detection model went from 89% to 94.7%. In addition, the employment of DataRobot Company’s ML software improved the accuracy of the Lenovo Company's sales forecast model by 7.5%.

By deployment, the cloud sector is anticipated to have a substantial share of the MLOps market.

The demand for cloud-based MLOps is increasing due to its scalability, flexibility, cost savings, accessibility, and ability to drive innovation. The deployment of cloud-based MLOps enables companies to easily deploy and manage ML models without heavy infrastructure investment or the need to maintain and update software. In addition, cloud-based MLOps are accessible from all geographical distances through stable internet connection aiding companies to collaborate with team members and partners located in different regions or time zones which is increasing the consumption of cloud-based MLOps in international businesses with distributed workforce or operations in multiple regions.

By end-user, the BFSI segment is projected to grow significantly over the forecast period.

MLOps is being extensively applied in the BFSI industry for various applications such as risk assessment, fraud detection, customer service, compliance, and portfolio management. They are used to assess and manage risks such as credit risk, market risk, and operational risk in the BFSI sector by using ML algorithms to analyze large volumes of data and identify potential risks to assist companies in making informed decisions. In addition, MLOps are being increasingly employed to ensure compliance with regulatory requirements such as anti-money laundering (AML) regulations and Know Your Customer (KYC) requirements to reduce the risk of non-compliance.

The deficiency of technical skills coupled with the high cost associated with MLOps can restrict the market growth.

The shortage of skilled professionals with technical expertise to manage and deploy ML models effectively is limiting the growth of the MLOps market. For instance, the State of Enterprise ML report generated in 2021 by DataRobot, an AI technology company revealed that organizations are consuming excessive time to install ML models, with approximately 64% of the organizations requiring more than a month. According to the same survey, 38% of the organizations employ more than 50% of their data scientists' work time to deploy ML models. In addition, the high implementation costs of the MLOps platform and services are restricting its adoption across small-sized enterprises as smaller businesses do not have the necessary resources to invest in expensive infrastructure and tools.

Market Developments:

In November 2022, ClearML, a company providing an MLOps platform, and Aporia, an ML monitor platform with customization features partnered to unveil a complete platform to aid DevOps teams, ML engineers, and data scientists in optimizing their ML pipelines by facilitating effective execution of various ML projects.

In December 2020, Google announced the introduction of new features to its Google Cloud AI Platform including MLOps capabilities such as model monitoring and continuous evaluation to allow developers and data scientists to monitor their ML models in real time and make adjustments as required.

In October 2020, Databricks, a leading software company in the US announced the launch of Machine Learning Runtime, a platform that included MLOps capabilities such as model tracking and versioning, enabling developers and data scientists to collaborate more effectively and streamline their ML workflows.

North America holds a substantial share of the MLOps market and is expected to expand over the forecast period.

The advancement of the IT network and the constant evolution in ML technology by leading technology companies such as Google, Amazon, and IBM are stimulating the growth of the MLOps in North America. Major IT companies in the US are investing heavily in Machine Learning Model Operationalization Management (MLOps) to provide solutions to their customers. For instance, AWS offers a range of MLOps tools and services, including Amazon SageMaker, a fully managed service that helps developers and data scientists build, train, and deploy machine learning models at scale. AWS also provides services such as Amazon S3 for data storage, Amazon Lambda for serverless computing, and AWS Glue for data preparation and ETL. In addition, the growing demand for ML solutions in various industries, including healthcare, finance, retail, and manufacturing, and the availability of cloud-based MLOps platforms and solutions in the region is anticipated to further develop the market over the forecast period.

Market Segmentation:

By Component

• Platform
• Services

By Deployment

• Cloud
• On-premise

By End User

• BFSI
• Manufacturing
• IT and Telecom
• Healthcare
• Media and Entertainment
• Others

By Geography

• North America
• USA
• Canada
• Mexico
• South America
• Brazil
• Argentina
• Others
• Europe
• Germany
• France
• United Kingdom
• Others
• Middle East and Africa
• Saudi Arabia
• UAE
• Others
• Asia Pacific
• China
• Japan
• India
• South Korea
• Taiwan
• Others