MLOps Market

Market Overview

The global MLOps market size was valued at USD 1.64 billion in 2023. It is estimated to reach USD 30.65 billion by 2032, growing at a CAGR of 38.45% during the forecast period (2024–2032). In recent years, there has been a rapid penetration of AI in several sectors as organizations worldwide increasingly recognize AI's transformative potential in driving operational efficiencies, enhancing customer experiences, and unlocking new revenue streams. This is estimated to drive the global MLOps market. Moreover, the introduction of new and advanced MLOps solutions is estimated to create opportunities for market growth.

Machine Learning Operations, or MLOps for short, is a collection of procedures designed to simplify machine learning model deployment, management, and ongoing enhancement in production settings. It combines aspects of DevOps, data engineering, and machine learning to ensure that ML models are deployed reliably and efficiently. MLOps involves automating model training, testing, deployment, and monitoring processes, enabling faster iteration and scaling of ML applications. It emphasizes collaboration between data scientists, developers, and operations teams to maintain model performance, scalability, and security over time. By integrating ML workflows with established software development practices, MLOps helps organizations effectively manage the lifecycle of machine learning models, from initial development to deployment and maintenance, ultimately accelerating innovation and maximizing the value of AI initiatives.

Highlights

• Platform dominates the component segment
• On-premises dominates the deployment segment
• BFSI dominates the vertical segment
• North America is the highest shareholder in the global market

Market Dynamics

Global MLOps Market Drivers

Rapid AI Adoption 

The exponential proliferation of artificial intelligence (AI) across industries is a primary driver propelling the growth of the MLOps market. The Worldwide Artificial Intelligence Spending Guide of International Data Corporation (IDC) indicated that global spending on AI will increase by 26.9% from 2022 to 2023, reaching USD 154 billion. It is anticipated that expenditure on AI-centric systems will exceed USD 300 billion by 2026, as the continuous integration of AI into a vast array of products generates a CAGR of 27.0% from 2022 to 2026.

With businesses increasingly leveraging AI-driven insights to enhance decision-making, optimize processes, and deliver personalized experiences, the demand for MLOps solutions surges. As AI becomes integral to digital transformation strategies, organizations seek robust MLOps platforms to operationalize ML workflows efficiently. This driver underscores the critical role of MLOps in enabling enterprises to harness the full prospect of AI and gain an edge in today's data-driven landscape.

Global MLOps Market Restraint

Lack of Skilled Professionals

Despite the promising growth trajectory, the MLOps market grapples with a significant constraint rooted in a scarcity of specialized talent. The intricate intersection of machine learning (ML) and DevOps demands professionals equipped with a unique skill set blending expertise in data science, software engineering, and infrastructure management. However, the current pool of such multifaceted professionals remains limited, posing challenges to the widespread adoption of MLOps practices across industries. This talent shortage hinders organizations' ability to effectively implement MLOps solutions and exacerbates competition for skilled personnel, leading to increased hiring costs and project delays.

Global MLOps Market Opportunities

Launch of Novel MLOps Solutions

In recent years, there has been a rise in the launch of novel and enhanced MLOps solutions. For instance, in November 2023, PostgresML, an AI extension for Postgres, recently unveiled its comprehensive platform for end-to-end machine learning operations (MLOps). PostgresML enables developers to create and implement AI applications on PostgreSQL by directly integrating cutting-edge machine learning and large language models into the widely-used relational database. This simplifies the process for app developers and offers various performance, cost, and quality benefits.

Furthermore, alwaysAI, a prominent leader in computer vision, announced the launch of advanced MLOps capabilities for its comprehensive computer vision platform in October 2023. By including these comprehensive functionalities, alwaysAI establishes itself as a leading MLOps provider, offering a range of robust features and tools to optimize the full computer vision lifecycle. This update enhances alwaysAI's current Dataset Management and Remote Deployment capabilities, simplifying the process for developers to construct, deploy, and oversee computer vision models and applications at an enterprise level. Thus, such factors are expected to create opportunities for market growth.

Regional Analysis

Based on region, the global MLOps market is bifurcated into North America, Europe, Asia-Pacific, Latin America, and the Middle East and Africa. 

North America Dominates the Global Market

North America is the most significant global MLOps market shareholder and is expected to expand substantially during the forecast period. North America is a dominant force in the MLOps market, buoyed by various factors. The region boasts a mature tech ecosystem, home to leading tech giants, innovative startups, and a robust venture capital landscape, fostering a fertile ground for MLOps innovation and adoption. With organizations across sectors embracing AI-driven initiatives to drive efficiency and innovation, the demand for MLOps solutions skyrockets. For instance, according to the 2023 Emerging Technology Survey conducted by PwC, 73% of US companies have already implemented artificial intelligence (AI) in certain aspects of their business. Moreover, North America leads in AI investments and research, further catalyzing MLOps market growth. According to Crunchbase, in 2023, over a quarter of investments in American startups were allocated to a company involved in AI.

Furthermore, there has also been a rise in fundraising to enhance the MLOps solutions. For instance, in December 2023, Featureform, the MLOps feature store for developing AI and ML systems, disclosed USD 5.5 million in Seed fundraising. The funding round was spearheaded by GreatPoint Ventures and Zetta Venture Partners, with contributions from Tuesday Capital and Alumni Ventures. The current round of capital infusion enables Featureform to enhance its product development and bolster its assistance to both existing and potential commercial clients and its open-source community. With the conclusion of the Seed round, Featureform has now raised USD 8.1 million in fundraising. Thus, the factors above are anticipated to augment the regional market expansion.

The Asia-Pacific region emerges as a hotbed of opportunity in the MLOps market, fueled by rapid digitization, burgeoning AI initiatives, and increasing cloud adoption. Countries like China, India, and Japan spearhead MLOps adoption, driven by a growing emphasis on data-driven decision-making and innovation. Moreover, there has been a rise in the key players' initiatives to promote the application of MLOps. For instance, in January 2024, TIER IV, a leader in open-source autonomous driving (AD) technology in Japan, announced the launch of the Co-MLOps (Cooperative Machine Learning Operations) Project.

This new initiative is focused on expanding the development of AI (Artificial Intelligence) for self-driving vehicles. The implementation of the Co-MLOps Platform, created as part of this project, would facilitate the worldwide dissemination of curated sensor data, such as camera images and LiDAR point clouds, obtained from different geographical areas. In addition, the Co-MLOps Platform will provide MLOps capabilities and Edge AI reference models, enabling partner organizations to improve their own AI systems for autonomous driving. Consequently, these factors are estimated to boost the regional market growth.

Segmental Analysis

The global MLOps market is bifurcated into component, deployment, organization size, and vertical.

Based on components, the global MLOps market is segmented into platforms and services. 

The platform segment owns the highest market share. In the MLOps market, platforms represent a pivotal segment offering comprehensive solutions to orchestrate the end-to-end ML lifecycle. These platforms encompass a suite of tools and frameworks designed to streamline ML model development, deployment, monitoring, and management, catering to the evolving needs of enterprises across industries. Key functionalities include version control, automated testing, model training, deployment automation, and performance monitoring.

Leading MLOps platforms, such as TensorFlow Extended (TFX), MLflow, and Kubeflow, provide scalable infrastructure, collaboration features, and integration capabilities with popular ML frameworks, empowering organizations to operationalize their ML initiatives effectively. With a focus on enhancing developer productivity, ensuring model reproducibility, and facilitating cross-functional collaboration, MLOps platforms emerge as indispensable enablers for driving AI innovation and delivering tangible business value in today's data-driven landscape.

Based on deployment, the global MLOps market is bifurcated into on-premises and cloud. 

The on-premises segment contributed to the largest market share. On-premises deployment of MLOps involves implementing machine learning operations within a company's infrastructure rather than relying on cloud-based solutions. It encompasses developing, deploying, monitoring, and managing machine learning models within the organization's data center or servers. This approach offers greater control, security, and compliance for sensitive data and regulatory requirements. However, it requires significant upfront investment in hardware, software, and expertise to set up and maintain the infrastructure.

On-premises MLOps enables seamless integration with existing systems and workflows, fostering collaboration among data scientists, engineers, and IT teams. It also allows for customized and optimized resources tailored to specific business needs. Thus, on-premises deployment offers autonomy and flexibility while ensuring data privacy and regulatory compliance.

Based on organization size, the global MLOps market is bifurcated into large enterprises and SMEs. 

The large enterprises segment dominates the global market. Large enterprises emerge as pivotal stakeholders driving the adoption of MLOps solutions, leveraging their scale, resources, and strategic imperatives to harness the transformative potential of AI. With expansive operations spanning diverse verticals, these organizations grapple with complex data environments and multifaceted ML workflows, necessitating robust MLOps frameworks for effective model management and governance. The scalability and customization capabilities offered by MLOps platforms resonate strongly with the needs of large enterprises, enabling them to orchestrate ML initiatives at scale, optimize resource allocation, and drive innovation across business functions. Moreover, MLOps solutions empower large enterprises to enhance agility, mitigate operational risks, and derive actionable insights from vast data reservoirs, fortifying their competitive edge in an increasingly data-driven landscape.

Based on vertical, the global MLOps market is bifurcated into BFSI, healthcare and life sciences, retail and e-commerce, IT and telecom, energy and utilities, government and public sector, media and entertainment, and others. 

The BFSI is estimated to own the highest market share. Within the MLOps market, the Banking, Financial Services, and Insurance (BFSI) sector emerges as a pivotal vertical, leveraging MLOps solutions to enhance operational efficiency, risk management, and customer experience. In banking, MLOps facilitates developing and deploying advanced analytics models for fraud detection, credit scoring, and personalized financial recommendations, empowering institutions to mitigate risks and drive revenue growth.

In the insurance domain, MLOps streamlines claim processing, underwriting, and actuarial modeling, enabling insurers to optimize pricing strategies, improve loss ratios, and enhance customer satisfaction. Moreover, stringent regulatory requirements necessitate robust model governance and compliance frameworks, fueling demand for MLOps platforms in the BFSI segment. MLOps becomes a strategic necessity as financial institutions proceed with their digital transformation path, allowing them to fully utilize AI-driven insights while maintaining operational resilience and regulatory compliance.

Recent Developments

• February 2024- KDnuggets unveiled Tech Briefs, its newest resource offering to the community. The subject matter of Tech Brief is Machine Learning Operations (MLOps).
• February 2024- JFrog announced that it integrated a managed machine learning operations (MLOps) platform from Qwak with its DevSecOps platform to facilitate collaboration between teams developing and deploying multiple classes of software artifacts.