Machine Learning Operations (MLOps) Market Size & Share Analysis - Trends, Drivers, Competitive Landscape, and Forecasts (2024-2030)

MLOps Market Size

MLOps Market Trends & Drivers

Induced Investments in AI/ML-Based Systems

With the objective of gaining competitive advantage through better customer insights, boosted employee productivity, the businesses across all industries globally are induced towards increasing their investment in AI/ML systems. Such automation systems can quickly and accurately analyze data and use complex algorithms to predict future steps, that eventually enhances the productivity of the enterprises. For instance, OTT platforms can suggest movies based on what its users have already seen.

Moreover, these systems have revolutionized the business management by consolidating workflow management tools and trend forecasting. Further, surge in investment in machine learning remarkably contributes toward the demand expansion. Furthermore, a large number of small start-ups and tech organizations have invested in the embracing of non–proprietary MLOps platforms to enhance efficiency in their value chains, which propels the growth at global level. Moreover, increase in availability of affordable, high-quality automated technology is expected to drive growth expansion in coming years.

Growth of AI in Healthcare

AI is based on technologies requires lots and lots of figures. Therefore, it becomes easier for the healthcare industry to use the advanced technology and track the record of their firm by a properly organized data setup. The medical information is of very high dimensional in character and vast in size and has thousands of attributes. The growing complex procedures led to the advent of machine learning in the healthcare industry that plays a major role in the healthcare industry through its problem-solving feature. The technology can improve the overall cost efficiency of healthcare services by using technology instead of manual processes during any health-related diagnosis and procedures.

ML is highly used in identifying the diseases and diagnosis of diseases and ailments, some of which are considered hard-to-diagnose. This technology in healthcare can be used for better diagnosis using various software and tools to analyze medical reports and images. For example, an ML algorithm can perform better pattern recognition and predict a disease based on training in similar cases. This field of automation can improve the overall cost efficiency of healthcare services by using technology in place of manual processes. The deployment of artificial intelligence tools and processes in the healthcare industry for better and faster outcomes, majorly for disease diagnosis are fostering the demand and growth of operationalization of ML.

MLOps is one facet of machine learning that is improving the way healthcare industry operates and it allows us to create a digitized healthcare system that is faster and more efficient which is further anticipated to render development.

Machine Learning Operations Market Analysis

Platform Segment Captures Largest Market Share

The platform component held the largest market value of USD 834 million and expect to grow with the CAGR of around 40% during the forecast period, owing to its attribute of optimizing the operation and management of machine learning models. It provides the organization help in building, training, managing, and deploying the models in a production-ready ML environment. It accelerates the business experiments with purpose-built tools, including classification, data preparation, training and tuning, monitoring, and various other activities. Further, it helps to improve the workflow from stats collection to application deployment in the real world, range from small-scale to enterprise-level cloud and open-source platforms.

Another reason behind the demand of MLOps platforms is the scalability and enhanced collaboration it provides in the organizational processes. Machine learning scalability involves scaling of ML applications that can handle any amount of information and execute many computations in a profitable and efficient way to instantly serve millions of users worldwide. Such platforms can be used by businesses with growing plans, rapid upscaling, or large amounts of figures to help them clean and prepare an insightful data.

BFSI Sector Holds Highest Number of Automated Applications

Machine learning and automation, cloud adoption, and data engineering are the broad category of solutions that are included in MLOps umbrella of solutions. The financial institutions can use these solutions to move beyond incremental developments and into the realm of radical innovation. The series of innovation include operationalization of ML is emerging as a crucial component of infrastructure for financial services businesses to take advantage of new opportunities as numbers volumes continue to grow rapidly with new technologies like IoT, AI, robotics, and conversational interfaces.

Machine learning adoption by financial sector has been on rise, majorly for solving specific challenges such as fraud detection, predictive maintenance of legacy systems, and yield management. Banks can automate the process of integrating AI/ML models into applications with the help of MLOps. This can be done without a drawback at any of the digital touchpoints or channels where customers interact with banks to improve their overall experiences.

With the help of these platforms, financial institutions are able to construct an infrastructure that is adaptable, nimble, and capable which can quickly scale up or down to meet spikes in demand. With minimal IT involvement, it enables financial sector to concentrate on tasks that are critical to the business. Therefore, the BFSI sector accounts for the largest market share of more than 18% in MLOps market. Such these platforms reduce implementation costs of integrating such system with self-managed environments with consistent code checks, version control, traceability, and app security requirements independent of continuous integration and continuous delivery pipelines. The execution of MLOps arranges the automated technologies to monitor payment transactions to provide signals that could indicate money laundering or the financing of terrorism.

Further, it allows financial institutions to train the models with their own information and statistics. As a result, they no longer need to outsource their stored facts and figures to third-party vendors, who frequently lack the ability to provide enterprise-grade data on a large scale. Thus, machine learning operation is handling uncertainty that brings an exponential level of efficiency to the business operated on the large-scale level, that can have direct positive consequences if executed correctly.

SMEs Have Larger Market Share

The small enterprises dominated the enterprise size segment in 2022 due to the large number of startups have invested in the adoption of MLOps platforms to increase productivity in their value chains. As per the US based information technology company, ML Platform startups funded more than USD 3 billion in research of different phases of machine learning operations that includes - Data Governance and monitoring, ML Platforms and monitoring, and Serving Platforms. Among all these, Data Monitoring is the second-most funded area of on these models with an approximate investment of USD 11 million till 2022. Also, the funding is followed by funding in ML Monitoring. Therefore, the adoption initiatives by the small and medium sized firms to bring efficiency makes the largest hold on the market.

Regional Analysis

North America Captured Lion’s Share in 2022

North America dominated the market share of 36% in 2022. The reason being strong R&D competences in AI in the developed economies and various leading AI companies and research institutes are based in this region. The increasing investment in advanced technologies to enhance the business operations and the customer experience are anticipated to provide lucrative growth opportunities in North America. These countries have strong research and development capabilities in AI over the past few years and invested heavily in AI-related technologies over the past few years. Also, they have implemented policies to support the development of the field.

However, Asia-Pacific is expected to exhibit highest growth during the forecast period. This is attributed to increase in digital penetration in and higher adoption of cloud-based services that propel the growth of the market in this region.

Market Breakdown

This report offers deep insights into the MLOps industry, with size estimation for 2019 to 2030, the major drivers, restraints, trends and opportunities, and competitor analysis.

Based on Component

• Platform
• Services

Based on Application

• Data Management
• Modeling

Based on Deployment Mode

• On-premise
• Cloud
• Hybrid

Based on End User

• Data Scientists
• ML Engineers
• AI enthusiasts
• Data Engineers

Based on Industry

• BFSI
• Manufacturing
• IT and Telecom
• Smart Mobility
• Retail and E-commerce
• Energy and Utility
• Healthcare
• Media and Entertainment

Geographical Analysis

• North America
  • U.S.
  • Canada
• Europe
  • Germany
  • U.K.
  • France
  • Italy
  • Spain
• Asia-Pacific
  • Japan
  • China
  • India
  • South Korea
  • Australia
• Latin America
  • Brazil
  • Mexico
• Middle East and Africa
  • Saudi Arabia
  • U.A.E.

Top MLOps Companies