Global MLOps Market Size, Share, Growth Analysis, By Infrastructure(Data Infrastructure and Model Infrastructure), By Data Management(Data Pipeline Management and Data Versioning) - Industry Forecast 2024-2031

Global MLOps Market size was valued at USD 1.10 billion in 2022 and is poised to grow from USD 1.55 billion in 2023 to USD 24.23 billion by 2031, growing at a CAGR of 41% during the forecast period (2024-2031).

MLOps, the technology facilitating the deployment of machine learning models at scale, is witnessing a surge in adoption across various sectors like healthcare, IT, and retail due to its capacity to address human errors and quality issues. This adoption trend is fueled by the increasing digital and internet penetration worldwide, presenting lucrative growth prospects for the MLOps market. Furthermore, enterprises are increasingly embracing MLOps to enhance their operations and productivity, further bolstering market growth. Notably, investments in the healthcare sector are anticipated to contribute significantly to market expansion in the forecast period. Despite the promising outlook, challenges such as inaccessible data, data security concerns, rigid business models, and a shortage of engineering skills pose obstacles to the growth of the MLOps market. Nevertheless, the ability of MLOps to streamline the machine learning lifecycle and reduce costs positions it for substantial growth in the coming years.

Top-down and bottom-up approaches were used to estimate and validate the size of the Global MLOps Market and to estimate the size of various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.

Global MLOps Market Segmental Analysis

The global MLOps market is segmented by infrastructure, data management, and region. Based on infrastructure, the market can be segmented into data infrastructure and model infrastructure. Based on data management the market is segmented into data pipeline management and data versioning. Based on region, the market is segmented into North America, Europe, Asia Pacific, Middle East and Africa, and Latin America.

Drivers of the Global MLOps Market

Manual data reprocessing and collection often yield unsatisfactory results, highlighting the inefficiency of traditional methods. MLOps offers a comprehensive solution by automating the entire machine learning model workflow, encompassing data gathering, model development, testing, retraining, and deployment. By implementing MLOps practices, companies can significantly save time and reduce error rates. Moreover, it fosters collaboration among IT and business personnel, data scientists, and engineers, facilitating the widespread adoption of machine learning models across the organization. With MLOps principles, businesses can standardize their ML operations and establish a unified language for all stakeholders, mitigating compatibility issues and accelerating the construction and deployment of modeling processes.

Restraints in the Global MLOps Market

Organizations are increasingly recognizing the importance of utilizing MLOps in data management to effectively gather and integrate vast volumes of data from various sources and break down data silos. However, the adoption of MLOps models is hindered by knowledge gaps and a lack of skilled workers within these organizations. As businesses often operate in silos, the need for MLOps becomes more critical to gain insights across multiple applications and verticals. Surveys consistently highlight the insufficient knowledge and capabilities of employees in handling MLOps, underscoring the urgency for investment in training and certifications. It is imperative for organizations to prioritize equipping their workforce with the necessary understanding and skills in MLOps models and strategies to ensure effective data management practices are implemented.

Market Trends of the Global MLOps Market

In the global MLOps market, significant trends include the integration of more automation features within MLOps platforms, such as auto-model selection and tuning, enabling businesses to streamline model development processes and cut down on time and costs. Additionally, there's a notable shift towards cloud-based MLOps platforms due to their scalability and flexibility, with cloud providers expanding their offerings to include services like automatic model deployment and monitoring. Effective MLOps relies on collaboration between data scientists and IT operations teams, prompting platforms to integrate collaboration features. Furthermore, as machine learning models grow in complexity, the trend of Explainable AI emerges, aiming to provide insights into the decision-making process of these models. Given the increasing use of sensitive data, security remains a top priority, leading MLOps platforms to incorporate robust security features to safeguard data and mitigate cyber threats.