MLOps Market Research, 2032
The global mlops market was valued at $1.4 billion in 2022, and is projected to reach $37.4 billion by 2032, growing at a CAGR of 39.3% from 2023 to 2032.
MLOps is a method based on adapting DevOps practices to machine learning development processes. MLOps is useful in transitioning from running a couple of ML models manually to using ML models in the entire company operation. Overall, MLOps helps to improve delivery time, reduce defects, and make data science more productive. This provides lucrative opportunities for market growth during the forecast period.
Moreover, MLOps is the missing bridge between machine learning, data science, and data engineering. It has emerged as the link that unifies these functions more seamlessly than ever before. It helps professionals and advanced systems to consistently deploy machine learning algorithms and solutions for enhanced productivity and effectiveness. The technology is based on the combination of an operating framework for people and technology, as well as, on an abidance for the best set of practices and proven architectural principles. In addition, MLOps empowers production-level machine learning. These emerging advanced MLOps applications solve a variety of human error and quality issues. As a result, many organizations such as healthcare, IT, retail, and other sectors have adopted MLOps due to its benefits. This factor creates lucrative growth opportunities in the market. For instance, in November 2022, Databricks launched MLflow 2.0, building upon MLflow's strong platform foundation and incorporating extensive user feedback to simplify data science workflows and deliver innovative, first-class tools for MLOps.
The surge in digital and internet penetration around the world is positively impacting the growth of the market. In addition, an increase in adoption of MLOps technology across enterprises to enhance operation & productivity strengthens the growth of the market for future. Furthermore, an increase in investments in the healthcare sector is expected to provide lucrative growth opportunities for the market during the forecast period. For instance, in May 2021, Google LLC launched a MLOps solution, Vertex AI a managed AI platform. It’s designed to help companies to accelerate the deployment and maintenance of AI models, Google says, by requiring nearly 80% fewer lines of code to train a model versus competitive platforms.
Moreover, MLOps helps reduce costs over the entire machine learning lifecycle, creating numerous opportunities for market growth in the upcoming years. However, inaccessible data & data security, rigid business models, and lack of engineering skills hamper the growth of the market.
The mlops market is segmented into Component, Deployment Mode, Organization Size and Industry Vertical.
Segment Review
The MLOps market is segmented into component, deployment mode, organization size, industry vertical, and region. By component, it is bifurcated into platform and service. By deployment mode, it is divided into on-premise and cloud. By organization size, the market is segregated into large enterprises and small & medium-sized enterprises. By industry vertical, the market is classified into BFSI, manufacturing, IT & telecom, retail & e-commerce, energy & utility, healthcare, media and entertainment and others. Region wise, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
On the basis of deployment mode, the on-premise segment captured the largest market share in 2022 and is expected to continue this trend throughout the forecast period. This is attributed due to the numerous advantages offered by the on-premise deployment such as a high level of data security and safety. Industries prefer on-premise model owing to high data security and less data breach as compared to cloud based deployment models, which further drive the demand for on-premise deployment model within the sectors. However, cloud segment is expected to exhibit highest growth during the forecast period. Factor such as rise in the adoption of cloud-based MLOps due to low cost and easier maintenance drives the growth of the market.
By region, North America dominated the market share in 2022 for the MLOps market. The increasing investment in advanced technologies such as AI and MLOps to improve businesses and the customer experience are anticipated to provide lucrative growth opportunities for the MLOps market in North America. However, Asia-Pacific is expected to exhibit highest growth during the forecast period. This is attributed to increase in penetration of digitalization and higher adoption of cloud-based services that propel the growth of the market in this region. For instance, in April 2023, China Academy of Information and Communications Technology launched the MLOps Tool Map and Rural Revitalization. This initiative encourages the transition of model production from decentralized small workshops to a large-scale and orderly factory assembly line model, which helps organizations improve the quality and efficiency of AI implementation.
COVID-19 Impact Analysis
MLOps market has witnessed significant growth in the past few years, and is expected to exhibit further notable growth, due to the outbreak of the COVID-19 pandemic. This is attributed to the surge in need of AI/ML-based project among enterprises for improving their customers’ needs and to upsurge their revenue opportunity. In addition, use of AI and machine learning technologies for managing growing volume of data generated from various devices in an organization drives the growth of the MLOps market. Many businesses invested in MLOps infrastructure to ease the increasing complexity of the IT needs of many business enterprises during the period.
According to a study, in the past few years, there has been a massive increase in the demand for data-related roles. The hiring for machine learning and artificial intelligence-related roles has grown by 74% annually. People from a multitude of backgrounds are trying to break into the data industry. Most of these individuals attempt to land a job in data science or analytics. However, there are many lesser-known career options in the data industry. MLOps sits at the intersection of data science, DevOps, and data engineering. An MLOps engineer brings machine learning models from test to production using software engineering and data science skills. Such factors showcased the growing opportunities for the MLOps market during the forecast period.
In addition, the MLOps and machine learning industry is advancing quite rapidly. Machine learning companies and tech service providers will continue to adopt MLOps and ML solutions. And the rising development of MLOps will trigger increased investments in machine learning. For instance, in May 2023, AIShield, the cutting-edge AI security start-up of Bosch, partnered with a leader in the Machine Learning Operations (MLOps) market, Databricks. By combining AIShield’s innovative AI security solutions and Databricks' powerful Machine Learning platform (MLOps), businesses benefit from enhanced protection for their AI-based applications. Such factors are anticipated to create numerous opportunities for market growth during the forecast period.
Top Impacting Factors
Rise of data-centric approach to MLOps:
A data-centric approach to building ML systems emphasizes data management over incremental modeling improvements. ML Systems are always a combination of data and code, but ML teams are increasingly spending their time iterating over data improvements over model improvements. Synthetic data, data labeling, validation, auditing, and ML monitoring become important parts of the MLOps process. It is becoming more common for a company to start with pre-trained models such as those offered through Amazon SageMaker or those offered through Hugging Face and implement ML monitoring before diving deeper into algorithmic improvements. The data-centric approach emphasizes data quality and monitoring as an essential part of building production systems and seeks to understand this data with code. Organizations taking on these techniques are more likely to engage in collaboration between teams, are less likely to over-engineer their machine learning code, and are likely to have better outcomes in production, thus driving the growth of the market.
Increase in the number of libraries and packages for MLOps tasks:
There is currently no single consensus on the use of a single MLOps tool or application. MLOps provides the capability to make ML systems adoptable by organizations regardless of their cloud providers or technical stacks, developing a one-stop solution that would be a substantial challenge. This is one of the major factors influencing the impact of the rise in the number of libraries and packages in MLOps on businesses.
Competition Analysis
The market players operating in the MLOps market are Akira AI, Alteryx, Amazon Web Services, Inc., Cloudera, Inc., Databricks, Inc., DataRobot, Inc., GAVS Technologies, Google LLC, IBM Corporation and Microsoft Corporation. These major players have adopted various key development strategies such as business expansion, new product launches, and partnerships, which help to drive the growth of the MLOps market globally.
Recent Partnership in the MLOps Market
In January 2022, IBM partnered with Francisco under this alliance Francisco acquired healthcare data and analytics assets from IBM that are currently part of the Watson Health business.
In July 2021, DataRobot partnered with Moviri, to building agile AI solutions catered to an established global customer base consisting of the most demanding enterprise organizations in financial services, media, telecommunications, retail, and manufacturing.
Recent Product Launch in the MLOps Market
In March 2023, Databricks launched Databricks Model Serving to provide simplified production machine learning (ML) natively within the Databricks Lakehouse Platform. Model Serving removes the complexity of building and maintaining complicated infrastructure for intelligent applications.
In November 2022, IBM launched new software namely, IBM Business Analytics Enterprise designed to help enterprises break down data and analytics silos so they can make data-driven decisions quickly and navigate unpredictable disruptions.
In June 2022, DataRobot launched Expanded AI Cloud capabilities and tools for data scientists, business users, MLOps, and DevOps. AI Cloud can access disparate data stores on multiple clouds to be managed all in one place.
Recent Acquisition in the MLOps Market
In July 2021, DataRobot acquired Algorithmia, further cementing DataRobot's place as the preeminent provider of comprehensive solutions in the MLOps space, focused on bringing machine learning models into production.
Key Benefits for Stakeholders
- The study provides an in-depth analysis of the MLOps market along with the current trends and future estimations to elucidate the imminent investment pockets.
- Information about key drivers, restrains, and opportunities and their impact analysis on the MLOps market size is provided in the report.
- The Porter’s five forces analysis illustrates the potency of buyers and suppliers operating in the MLOps industry.
- The quantitative analysis of the global sports management market for the period 2022–2032 is provided to determine the MLOps market potential.
MLOps Market Report Highlights
| Aspects | Details |
 Market Size By 2032 | USD 37.4 billion |
 Growth Rate | CAGR of 39.3% |
 Forecast period | 2022 - 2032 |
 Report Pages | 278 |
 By Component |
|
 By Deployment Mode |
|
 By Organization Size |
|
 By Industry Vertical |
|
 By Region |
|
 Key Market Players | IBM Corporation, Alteryx, Databricks, Inc., DataRobot, Inc., Microsoft Corporation, GAVS Technologies, Akira AI, Cloudera, Inc., Amazon Web Services, Inc., Google LLC |
Analyst Review
Over time, businesses have seen various changes in the business processes, operations, and industrial automation. Moreover, businesses are shifting towards the digital platform and increasing implementation of Industry 4.0 to cope with ongoing tough business competition, which creates the need for seamless solution and platform to meet these requirements. This increases eventually escalates the adoption of MLOps in industry rapidly. Furthermore, artificial intelligence (AI) and machine learning (ML) technologies become more widely used in various sectors, which provide lucrative opportunities for market growth during the forecast period.
On the contrary, AI and ML solutions are expected to increasingly make an impact on global industries, business leaders need to track and quantify the value of models in use. This visibility is quite useful for the health, security, and reputation of organizations using the models. These processes include a methodology to track model quality, visibility, and reusability by measuring the difference in effort for code development through MLOps.
Moreover, MLOps expands beyond the process to transform processes, people, and technologies. People participation is a key ingredient to successfully implementing MLOps. It is crucial to form a group of business leaders or key stakeholders from different levels and departments to develop best practices, which provide lucrative growth opportunities for market growth in the upcoming years. For instance, in May 2022, Quantiphi, an AI-first digital engineering firm, launched the centralized MLOps platform, “NeuralOps” to help organizations overcome challenges in large-scale ML operations. This platform allows enterprises to organize their ML assets and streamline ML solution development and deployment.
Furthermore, the MLOps and machine learning industry is advancing quite rapidly. Based on reports, machine learning companies and tech service providers will continue to adopt MLOps and ML solutions. And the rising development of MLOps will trigger increased investments in machine learning.Nevertheless, prominent market players are exploring new technologies and applications to meet the increase in customer demands. Product launches, collaborations, and acquisitions are expected to enable them to expand their product portfolios and penetrate different regions.
For instance, in March 2023, FedML, a decentralized and collaborative machine learning platform that enables training, deployment, and continual improvement of AI models anywhere, partnered with Konica Minolta, a Japanese multinational technology company. In this partnership, FedML is expected to empower Konica Minolta to launch collaborative and privacy-preserving training, serving, and monitoring of ML models for medical imaging and radiomics across decentralized data silos with diverse annotation quality. In addition, the partnership is anticipated to also help to strengthen FedML’s platform for further MLOps adoptions in the healthcare domain.
The surge in digital and internet penetration around the world is positively impacting the growth of the market. In addition, an increase in adoption of MLOps technology across enterprises to enhance operation & productivity strengthens the growth of the market for future.
The North America is the largest market for the MLOps.
MLOps Market is expected to reach $37,398.46 Million, by 2032.
The key growth strategies for MLOps include product portfolio expansion, acquisition, partnership, merger, and others.
The top players operating in the MLOps market are Akira AI, Alteryx, Amazon Web Services, Inc., Cloudera, Inc., Databricks, Inc., DataRobot, Inc., GAVS Technologies, Google LLC, IBM Corporation and Microsoft Corporation.
Loading Table Of Content...
Loading Research Methodology...
