Machine Learning Operations (MLOps) Market Size, Share, Growth, and Industry Analysis, By Grade (On-premise, Cloud and Others), By Application (BFSI, Healthcare, Retail, Manufacturing, Public Sector and Others), and Regional Insights and Forecast to 2031

Updated On: 22 April 2024

machine learning operations (MLOps) market Report Overview

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The global machine learning operations (MLOps) market size was USD 1117.7 million in 2022 and will reach USD 9066.7 million by 2029, at CAGR of 41.8% during the forecast period. The global COVID-19 pandemic has been unprecedented and staggering, with the machine learning operations (MLOps) market experiencing higher-than-anticipated demand across all regions compared to pre-pandemic levels. The sudden rise in CAGR is attributable to the market's growth and demand returning to pre-pandemic levels once the pandemic is over.

Machine learning operations (MLOps) is a term that refers to the best practices for businesses to run artificial intelligence (AI) successfully with the help of software products and cloud services. MLOps is a combination of machine learning and the continuous development practice of DevOps in the software field. MLOps aims to deploy and maintain machine learning models in production environments reliably and efficiently.

 Machine learning operations (MLOps) also involves automating and standardizing the processes across the machine learning lifecycle, such as data preparation, model training, testing, integration, release, and monitoring12. Machine learning operations (MLOps) is a collaborative function that requires the coordination and alignment of different stakeholders, such as data scientists, data engineers, software engineers, DevOps engineers, business analysts, product managers, and end users.

COVID-19 Impact: Pandemic Increased the Market Demand owing to the Increase in the Demand in Various Industries

The COVID-19 pandemic had a significant impact on the machine learning operations (MLOps) market share. The pandemic increased the demand for machine learning solutions in various domains, such as healthcare, education, e-commerce, and social media. These domains require Machine learning operations (MLOps) platforms and services to manage and scale their machine learning models effectively and efficiently. For example, healthcare organizations use MLOps to deploy and monitor models for diagnosis, prognosis, drug discovery, and vaccine development1. Similarly, e-commerce platforms use MLOps to optimize their recommendation systems, inventory management, and customer service

Latest Trends

"Emergence Of Cloud-Based MLOps Platforms and Services is expected to Fuel the Market Growth"

One of the recent trends in the MLOps market is the emergence of cloud-based MLOps platforms and services. Cloud-based Machine learning operations (MLOps) platforms and services offer several advantages over on-premises solutions, such as lower cost, higher scalability, faster deployment, easier integration, and better security. Cloud-based MLOps platforms and services also enable organizations to leverage the expertise and resources of cloud providers, such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), IBM Cloud, and Alibaba Cloud. These cloud providers offer various tools and frameworks for building, deploying, and managing machine learning models on their platforms425. For example, AWS offers SageMaker, Azure offers Machine Learning, GCP offers AI Platform, IBM Cloud offers Watson Studio, and Alibaba Cloud offers PAI. These tools and frameworks provide features such as data ingestion, preprocessing, feature engineering, model training, testing, validation, deployment, monitoring, retraining, governance, and collaboration. Cloud-based MLOps platforms and services are expected to grow at a higher rate than on-premises solutions in the coming years.

machine learning operations (MLOps) market Segmentation

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• By Grade Analysis

According to type, the market can be segmented into On-premise, Cloud and Others.

• By Application Analysis

Based on age, the market can be divided into BFSI, Healthcare, Retail, Manufacturing, Public Sector and Others.

Driving Factors

"Increasing Complexity and Diversity of Machine Learning Models to Foster the Market Growth"

One of the driving factors for the market growth is the increasing complexity and diversity of machine learning models. Machine learning models are becoming more complex and diverse in terms of architectures, algorithms, parameters, inputs, outputs, performance metrics, and use cases. These models require more sophisticated methods and tools to manage their lifecycle stages from development to deployment to maintenance. MLOps platforms and services provide such methods and tools to handle the complexity and diversity of machine learning models. They enable organizations to standardize their machine learning workflows across different teams and projects. They also enable organizations to automate their machine learning processes from data preparation to model deployment to model monitoring. They also enable organizations to optimize their machine learning performance by providing feedback loops for model improvement

"Increasing Need for Collaboration and Alignment among Different Stakeholders to Propel Market Growth"

Another driving factor for the MLOps market growth is the increasing need for collaboration and alignment among different stakeholders involved in machine learning projects. Machine learning projects involve various stakeholders with different roles and responsibilities, such as data scientists, data engineers, software engineers, DevOps engineers, business analysts, product managers, and end users. These stakeholders have different goals, expectations, and perspectives on machine learning models. They also have different skills, tools, and workflows for working with machine learning models. MLOps platforms and services provide a common platform and language for these stakeholders to collaborate and align their efforts on machine learning projects. They enable these stakeholders to share data, code, models, metrics, and insights across different stages of machine learning lifecycle.

Restraining Factor

"Lack of Standardization and Interoperability Cost to Hamper the Market Growth"

One of the restraining factors for the market growth is the lack of standardization and interoperability among different MLOps platforms and services. MLOps platforms and services are developed and offered by various vendors, such as cloud providers, software companies, and startups. These vendors have different approaches, designs, and implementations of MLOps platforms and services. They also have different features, functions, and interfaces for their MLOps platforms and services. This leads to a lack of standardization and interoperability among different MLOps platforms and services.

machine learning operations (MLOps) market Regional Insights

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"North America to Lead the Market Owing to a Strong Presence of Leading Players"

The North America region has shown the highest machine learning operations (MLOps) market growth. North America has a strong presence of leading players in the MLOps market, such as AWS, Microsoft, Google, IBM, and Databricks. These players offer various MLOps platforms and services to their customers across different industries and domains. They also invest heavily in research and development of new and innovative MLOps solutions. They also collaborate with other players in the ecosystem, such as academia, startups, and partners, to promote and advance the adoption of MLOps.

Key Industry Players

"Key Players Are Employing Advanced Technologies In Order To Stimulate Further Growth Of The Market  "

All the major players are motivated to offer superior and more advanced services in order to gain a competitive edge in the market. To increase their market presence, vendors are using a variety of techniques, including product launches, regional growth, strategic alliances, partnerships, mergers, and acquisitions.

List of Market Players Profiled

• IBM (U.S)
• DataRobot (U.S)
• SAS (U.S)
• Microsoft (U.S)
• Amazon (U.S)
• Google (U.S)
• Dataiku (France)
• Databricks (U.S)
• HPE (U.S)
• Iguazio (Israel)
• ClearML (Israel)
• Modzy (U.S)
• Comet (U.S)
• Cloudera (U.S)
• Paperspace (U.S)
• Valohai (Finland)

Report Coverage

This report examines an understanding of the machine learning operations (MLOps) market’s size, share, growth rate, segmentation by type, application, key players, and previous and current market scenarios. The report also collects the market’s precise data and forecasts by market experts. Also, it describes the study of this industry’s financial performance, investments, growth, innovation marks, and new product launches by the top companies and offers deep insights into the current market structure, competitive analysis based on key players, key driving forces, and restraints that affect the demand for growth, opportunities, and risks.

Furthermore, the post-COVID-19 pandemic’s effects on international market restrictions and a deep understanding of how the industry will recover, and strategies are also stated in the report. The competitive landscape has also been examined in detail to provide clarification of the competitive landscape.

This report also discloses the research based on methodologies that define price trend analysis of target companies, collection of data, statistics, target competitors, import-export, information, and previous years’ records based on market sales. Moreover, all the significant factors which influence the market such as small or medium business industry, macro-economic indicators, value chain analysis, and demand-side dynamics, with all the major business players have been explained in detail. This analysis is subject to modification if the key players and feasible analysis of market dynamics change.