Machine Learning Model Operationalization Management (MLOPS) Market Size, Share, Opportunities, And Trends By Component (Platform, Services), By Deployment (Cloud, On-premise), By End User (BFSI, Manufacturing, IT And Telecom, Healthcare, Media And Entertainment, Others), And By Geography - Forecasts From 2023 To 2028

MLOps Market Size:

The machine learning model operationalization management (MLOPS) market is estimated at US$7.778 billion in 2025 and is expected to grow at a CAGR of 16.16% to attain US$16.449 billion by 2030.

MLOps Market Highlights:

• Rapid Adoption: The MLOps market grows as enterprises scale AI deployment and automation.
• Platform Dominance: Integrated platforms streamline model development, deployment, and monitoring processes.
• Cloud Preference: Cloud-based MLOps solutions lead due to scalability and cost-efficiency.
• Automation Surge: Automated pipelines reduce model deployment time and enhance performance.

MLOps Market Trends:

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 management solution also helps in accelerating the machine learning deployment model.

The major factor propelling the global MLOPS market growth is the increasing global adoption of AI technology. AI boosts the capability of MLOps to effectively deploy, manage, and monitor ML models. The adoption of MLOps is essential to companies as it ensures that the models developed by data scientists can generate accurate predictions and recommendations in real-world scenarios. The Federal Reserve Bank of St. Louis of the USA, in its report, stated that as of August 2024, about 40% of the total population of the US, aged between 16 and 64 years, uses the generative AI platform.

In addition, they help enterprises 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, which 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 MLOps market growth as companies need to ensure that their machine learning models are secure and compliant with regulations.

MLOps Market Drivers:

• Increasing adoption rate of machine learning

The increasing adoption of machine learning by enterprises to improve their operations and gain a 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%.

• The growing demand for industrial automation is propelling the market’s growth

The increasing demand for industrial automation is among the key factors pushing the global MLOPS market growth during the forecasted timeline. With the rising demand for industrial automation, MLOPS solutions help automate the implementation process of deep learning and machine learning platforms.

The MLOPS solution also helps enhance the manufacturing sector's efficiency and offers scalability. MLOPS solution also allows faster model development and helps in delivering efficient and higher-quality ML models.

The global industrial automation landscape has witnessed major growth, especially in recent years. In its global report, the International Federation of Robotics stated that the sale of industrial or factory robots witnessed a record sale in 2024.

The agency stated that in September 2024, about 4,281 thousand factory robots were operating globally. The agency further stated that the operational stock of industrial robots in 2021 was recorded at 3,479 thousand, which surged to 3,904 thousand in 2022.

MLOps Market Segmentation Analysis:

• 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 a stable internet connection, aiding companies to collaborate with team members and partners located in different regions or time zones. This is increasing the consumption of cloud-based MLOps in international businesses with distributed workforces or operations in multiple regions.

Major providers like AWS, Microsoft Azure, and Google Cloud have invested heavily in cloud-native MLOps tools, making them the preferred choice for enterprises adopting AI at scale. Cloud deployments lead the MLOps market due to their ability to handle large-scale data processing and model training without requiring significant upfront investment in hardware. They also support dynamic scaling, which is essential for handling variable workloads in AI applications. A 2024 report by Microsoft Azure notes that 70% of enterprises deploying MLOps solutions prefer cloud-based systems due to their ability to integrate with existing cloud ecosystems and support real-time model updates. Additionally, cloud deployments facilitate collaboration across geographically distributed teams, a critical factor for global organizations.

• 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. As per the data from UK Finance, a loss of £571.7 million in the UK was due to fraudsters in the first half of 2024, which was a rise of about 16% from 2023.

The largest loss was due to authorized push payment fraud, which accounted for £213.7 million, while the loss due to payment cards accounted for £277.7 million. Further, the loss due to remote banking and cheques accounted for £76.5 million and £3.8 million, respectively, in the first half of 2024.

With the rise in digital transactions, the BFSI industry requires MLOPS to analyze transaction data in real-time and perform screening of transactions assigned to detect any unauthorized activities on customer accounts.

For instance, the Indian Government and the RBI brought together industry stakeholders to enhance digital payments, with the volumes under UPI pegged at 2,762 in FY25, while India currently possesses 46% of global digital transactions. This will lead to a rise in the MLOPS market requirements in the coming years.

 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.

• By component, platforms are expected to witness significant growth

MLOps platforms are the cornerstone of the MLOps ecosystem, providing integrated tools to automate and manage the machine learning lifecycle, including model development, deployment, monitoring, and governance. These platforms are critical for organizations seeking to scale AI initiatives, as they unify disparate processes such as data preparation, model training, and continuous integration/continuous deployment (CI/CD). The dominance of platforms in the MLOps market stems from their ability to reduce complexity, enhance collaboration between data scientists and engineers, and ensure model reliability in production.

Platforms are the leading component in the MLOps market due to their comprehensive functionality and widespread adoption across industries. They address challenges such as model drift, reproducibility, and scalability, which are critical for enterprise-grade AI deployments. For instance, platforms like Databricks and Google Cloud’s Vertex AI have gained traction for their ability to integrate with existing cloud ecosystems and provide end-to-end MLOps capabilities. A 2024 analysis by Google Cloud highlights that organizations using integrated MLOps platforms report a 30% reduction in model deployment time and a 25% improvement in model performance due to automated monitoring and retraining pipelines. Platforms dominate due to their ability to integrate with diverse data sources, support multiple frameworks (e.g., TensorFlow, PyTorch), and provide scalability for enterprise needs. They also enable cross-functional collaboration, reducing the gap between data science and IT operations. As organizations prioritize AI-driven decision-making, platforms are expected to maintain their lead in the MLOps market.

For instance, Databricks enhanced its MLOps platform with Unity Catalog, enabling centralized governance and lineage tracking for machine learning models. This addresses compliance needs in regulated industries like finance and healthcare, where model transparency is critical. Tools like MLflow, an open-source MLOps platform, have seen increased adoption, with substantial downloads in 2024, reflecting the demand for flexible, vendor-agnostic solutions.

Recent advancements in platforms emphasize automation of model retraining and hyperparameter tuning. For example, AWS SageMaker’s 2025 update introduced automated drift detection, reducing manual intervention by 40% in large-scale deployments.

MLOps Market Restraints:

• The deficiency of technical skills, coupled with the high cost associated with MLOps, can restrict 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.

MLOps Market Geographical Outlook:

• 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 MLOps market growth 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 are anticipated to further develop the market over the forecast period.

The USA MLOps market is experiencing growth primarily due to a rise in the adoption of AI. AI is utilized in MLOPS for diverse purposes, such as ensuring effective management, deployment, and monitoring of the AI models in diverse sectors.

According to data from the US Census BTOS, the adoption of AI tools by firms differs across diverse industries in the country, with the largest percentage share in the information industry at 18.1%. This was followed by professional, scientific, and technical services accounting for 12%, educational services valued at 9.1%, and finance and insurance at 6.9% as of 2024.

These high adoption rates in the specific sectors indicate a high demand for MLOPS solutions in the coming years that are tailored to their unique requirement, leading market players to focus on developing specialized tools and platforms that will contribute to regional market growth.

Additionally, the highest adoption rates of AI by firms also differ across states, with the highest adoption state being Colorado, accounting for 7.4%, and Florida at 6.6%. Utah, Nevada, and Delaware are all valued at 6.5% separately.

The adoption of MLOPS across industries and states is crucial for successful AI implementation. Businesses need MLOPS solutions to manage the model lifecycle, from development to deployment, resulting in a significant demand for MLOPS platforms and services in the USA.

The USA's significant fraud and scam losses are driving demand for advanced MLOPS solutions that enable real-time fraud detection, continuous model optimization, and secure data handling. For instance, Salesforce, Inc. data reported the fraud loss reported in the country was valued at US$2,109.6 million in 2024 through payment apps and services.

MLOps 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. These capabilities will allow developers and data scientists to monitor their ML models in real-time and make adjustments as required. 

 

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

The MLOps Market is segmented and analyzed as:

• 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

1. EXECUTIVE SUMMARY

2. MARKET SNAPSHOT

2.1. Market Overview

2.2. Market Definition

2.3. Scope of the Study

2.4. Market Segmentation

3. BUSINESS LANDSCAPE

3.1. Market Drivers

3.2. Market Restraints

3.3. Market Opportunities

3.4. Porter’s Five Forces Analysis

3.5. Industry Value Chain Analysis

3.6. Policies and Regulations

3.7. Strategic Recommendations

4. MACHINE LEARNING MODEL OPERATIONALIZATION MANAGEMENT (MLOPS) MARKET BY COMPONENT

4.1. Introduction

4.2. Platform

4.3. Services

5. MACHINE LEARNING MODEL OPERATIONALIZATION MANAGEMENT (MLOPS) MARKET BY DEPLOYMENT

5.1. Introduction

5.2. Cloud

5.3. On-premise

6. MACHINE LEARNING MODEL OPERATIONALIZATION MANAGEMENT (MLOPS) MARKET BY END-USER

6.1. Introduction

6.2. BFSI

6.3. Manufacturing

6.4. IT and Telecom

6.5. Healthcare

6.6. Media and Entertainment

6.7. Others

7. MACHINE LEARNING MODEL OPERATIONALIZATION MANAGEMENT (MLOPS) MARKET BY GEOGRAPHY

7.1. Introduction

7.2. North America

      7.2.1. USA

      7.2.2. Canada

7.2.3. Mexico

7.3. South America

            7.3.1. Brazil

            7.3.2. Argentina

            7.3.3. Others

7.4. Europe

            7.4.4.1. Germany

            7.4.4.2. France

            7.4.4.3. United Kingdom

            7.4.4.4. Others

7.5. Middle East and Africa

            7.5.1. Saudi Arabia

            7.5.2. UAE

            7.5.3. Others

7.6. Asia Pacific

            7.6.1. China

            7.6.2. Japan

            7.6.3. India

            7.6.4. South Korea

            7.6.5. Taiwan

            7.6.6. Others

8. COMPETITIVE ENVIRONMENT AND ANALYSIS

8.1. Major Players and Strategy Analysis

8.2. Emerging Players and Market Lucrativeness

8.3. Mergers, Acquisitions, Agreements, and Collaborations

8.4. Competitive Dashboard

9. COMPANY PROFILES

9.1. IBM

9.2. Microsoft

9.3. Amazon Web Services

9.4. Databricks

9.5. Google LLC

9.6. Fractal Analytics Inc.

9.7. Cloudera

9.8. Hewlett-Packard Enterprise Development LP

9.9. DataRobot, Inc.

9.10. Neptune Labs

9.11. Opsio

 

9.12. Softweb Solutions Inc. (Avnet Company) 

IBM

Microsoft

Amazon Web Services

Databricks

Google LLC

Fractal Analytics Inc.

Cloudera

Hewlett-Packard Enterprise Development LP

DataRobot, Inc.

Neptune Labs

Opsio

 

Softweb Solutions Inc. (Avnet Company)