MLOps Market Report Scope & Overview:

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The MLOps Market Size was valued at USD 1.3 Billion in 2023. It is expected to grow to USD 29.6 Billion by 2032 and grow at a CAGR of 41.6% over the forecast period of 2024-2032.

The ability to integrate MLOps with existing DevOps is becoming more of a standard practice among organizations aiming to enhance the effectiveness of their machine learning work. By adopting DevOps practices, organizations can improve the agility and reliability of their machine learning deployments. This integration includes several practices that make MLOps a part of DevOps, such as the use of version control to provide insights into the changes in machine learning models and data and allow for the reversal of changes. Next, the use of CI/CD pipelines enables automated testing and deployment processes and quick iteration as well as minimal deployment issues or downtime.

The process of rolling out updates ensures that models are always tested against performance metrics, and deploying them is their final validation step. Last, monitoring models that are already in production reveals such issues as data drift or model degradation and allows for the maintenance of optimum performance. By combining MLOps with DevOps, organizations can create a positive, encouraging, and innovative culture that allows them to deliver more effective and coherent machine learning solutions.

The U.S. Bureau of Labor Statistics projects that employment in computer and information technology occupations, which includes AI and ML fields, will grow 15% from 2021 to 2032, much faster than the average for all occupations, highlighting the increasing relevance of these technologies in business.

With the rising realization of the transformative effects of artificial intelligence and machine learning on enhancing business innovation and operational efficiency, there is an accelerating demand for MLOps solutions. The latter is indispensable for streamlining the deployment, monitoring, and management of ML models at scale. Since businesses aspire to derive competitive advantage from data-driven insights, one can no longer ignore the complexity of effectively managing multiple models in production. MLOps is a structured framework that harnesses best practices from development and operations, implying that the models are not only deployed fast but also constantly monitored whether they effectively and reliably perform. According to a 2022 report by the National Institute of Standards and Technology (NIST), 61% of U.S. businesses reported adopting AI technologies, with many indicating that these technologies are critical for enhancing efficiency and operational processes.

MLOps Market Dynamics

Drivers

• Increasing adoption of machine learning and artificial intelligence.

• The increase in internet and digital adoption around the world has a beneficial effect on market expansion.

• Growing demand for cloud-based MLOps solutions.

The increasing demand for cloud-based MLOps solutions that can help companies organize their machine learning operations. Due to the growing adoption of AI and ML technologies among various organizations, the issue of managing multiple ML models has become especially relevant. Cloud-based MLOps solutions provide companies with a seamless way to address this problem. Firstly, they ensure high efficiency and scalability of the infrastructure. Thus, based on the volume of uploaded data, models, and required computational capacity, organizations dynamically manage resources implemented in the cloud, gaining considerable benefits in terms of managing the workload. Secondly, by running all necessary tools in the cloud, the platforms enhance collaboration between data scientists, engineers, and business specialists.

It is because of the nature of cloud environments, knowledge workers can access the necessary tools from any location to run their experiments. The sphere of machine learning, as well as business in general, is characterized by the demand for high speed, hence the ability to quickly conduct experiments and adjust models. Finally, cloud providers can ensure the highest level of security and design robust compliance frameworks. In conclusion, the adoption of AI and ML will only increase in the upcoming years, and the demand for cloud-based MLOps platforms is expected to grow as well. 

A report from the National Institute of Standards and Technology (NIST) noted that over 70% of organizations are integrating AI into their cloud-based platforms, reflecting the growing reliance on cloud environments for AI and machine learning operations.

Restraint

• Complexity of MLOps

• Lack of awareness about MLOps among many organizations.

One of the most substantial constraints to machine learning practices and technologies is the lack of awareness about MLOps in many organizations. Despite the increasing recognition of artificial intelligence and machine learning as potent tools for helping companies expand and improve their operations MAI21, many companies, particularly small and medium enterprises, do not have adequate reasons to learn about the benefits of MLOps, which can result from a variety of factors. Some of these include a scarcity of educational opportunities, a lack of experience in data science, or the absence of a compelling case demonstrating the advantages of MLOps. Accordingly, decision-makers might undervalue the role such an operation framework may play in the process of deploying, monitoring, and managing ML models.

Opportunity

• The rise of edge computing

• Demand for MLOps tools and solutions presents opportunities for companies to develop and provide innovative MLOps tools and platforms.

Edge computing is a new paradigm for computing that brings computation and storage closer to the data source. This is creating new opportunities for MLOps, as it allows organizations to deploy and run ML models at the edge. The advent of DNA microarray technology made possible screening a relatively large number of SNPs simultaneously. This technology is crucial in population screening for susceptibility to multifactorial diseases. Technology is very useful in the diagnosis of hemoglobinopathies simultaneously. DNA sequences can be tested on one slide and multiple people evaluated at a relatively low cost. Understanding various single nucleotide polymorphisms present in each of the multiple genes evaluated would not have been possible a few years ago. DNA microarray technology has enabled a revolution in the genetic testing of a variety of diseases.

MLOps Market Segmentation Overview

By Component

The platform segment held the largest market share of over 64% as of 2023. MLOps platforms are offered as comprehensive tools with a variety of integrated services, covering the whole range of tasks necessary to support this life cycle. In this way, data preparation, model training, deployment, and monitoring can all be carried out with such solutions, including such vital stages as model training and data preparation. They help enhance the collaboration of data scientists, engineers, IT departments, and others, ensuring that all of the parties involved experience simplified and more facilitated operations. More importantly, this approach contributes to a substantial decrease in time needed to start running machine learning applications or models, and, providing such features as version control, automated tests, and CI/CD, platforms also contribute to the reliability, scalability, and stability of the solutions offered.

By Deployment

The cloud segment held the largest market share around 42% in 2023. Cloud-based solutions are widespread nowadays due to the scalability and flexibility they provide. This is a feature an organization can manage and deploy machine learning solutions without the necessity of having a vast on-the-ground facility. Speaking of scalability, for example, it is important for enterprises with a vast workload and huge datasets as cloud solutions smoothly allocate necessary resources and maximize them to meet the demand. In addition, companies that supply clouds might offer a range of tools stretching from data storage to training and model facilitating its deployment, thus, appealing to businesses due to the wide options available. In addition, the cloud is a proper environment for collaboration since it is easily accessible to professionals from different parts of the world

By End-Use

IT & Telecom segment held the largest market share around 30% in 2023. IT and telecom are dominant in their specific features that make the demand for solutions and products they have very high. It can mainly be explained by the intensive development of such technologies as cloud computing, artificial intelligence, the Internet of Things, etc., that require robust IT and telecom solutions. Companies operating in the segment have to look for new and more efficient ways of working, seek to cut costs and improve the quality of products and services. The current trend for digital communication and the across-the-board introduction of 5G encourages those companies to create new products and services, hence, leading to the further development of their MLOps.

MLOps Market Regional Analysis

North America dominated the market in 2023, accounting for over 44% of the worldwide revenue. Due to Al's strong R&D capacities in developed economies, research institutions, and numerous top Al firms situated in this area. It is projected that North America will see profitable growth prospects as a result of the rising investment in cutting-edge technologies to improve customer experience and business processes. Additionally, over the past few years, the region has made significant investments in technology related to aluminumw2 and has strong R&D skills in the field. To assist the advancement of the field, they have also implemented policies. For instance, open-source business Allegro AI announced in December 2022 that it had passed a significant growth milestone, setting new benchmarks in user base, revenue, and collaborations. The company also announced opening its first office in the U.S. to meet the high demand for its platform

Asia Pacific is expected to have the highest CAGR over the forecast period. Cloud computing in the area is expanding quickly, with major businesses like Amazon Web Services, Inc., Microsoft, and Google increasing their presence there. As enterprises take use of cloud infrastructure's scalability and flexibility, cloud-based MLOps solutions are anticipated to experience increasing adoption in the region. Additionally, governments and companies in the APAC region are making significant investments in AI and machine learning. This investment increases demand for MLOps solutions, which enable businesses to rapidly build and use machine learning models.

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Key Players in MLOps Market

• IBM Corporation (IBM Watson Studio)

• GAVS Technologies (MLOps.ai)

• Amazon Web Services, Inc. (Amazon SageMaker)

• Databricks, Inc. (Databricks Unified Analytics Platform)

• DataRobot, Inc. (DataRobot MLOps)

• Microsoft Corporation (Azure Machine Learning)

• Cloudera, Inc. (Cloudera Machine Learning)

• Akira AI (Akira AI Platform)

• Alteryx (Alteryx Designer)

• Google LLC (Google AI Platform)

• H2O.ai (H2O Driverless AI)

• NVIDIA Corporation (NVIDIA Triton Inference Server)

• Tecton (Tecton Feature Store)

• Paperspace (Paperspace Gradient)

• Kubeflow (Kubeflow Pipelines)

• MLflow (MLflow Tracking)

• Seldon Technologies (Seldon Core)

• ClearML (ClearML Platform)

• Weight & Biases (WandB)

• Neptune.ai (Neptune.ai)

Key User in MLOps Market

• Google

• Facebook (Meta Platforms, Inc.)

• JPMorgan Chase

• Goldman Sachs

• Mayo Clinic

• Philips Healthcare

• Walmart

• Amazon

• AT&T

• Verizon

• Tesla

Recent Development:

• In April 2023, Canonical Ltd., launched Charmed Kubeflow, its machine learning operations toolkit, on Amazon Web Services Inc.’s cloud marketplace. The new launch is planned for businesses looking to kickstart their ML and AI initiatives.

• In 2023, AWS introduced new features in Amazon SageMaker, including SageMaker Canvas for visual data science and SageMaker Model Registry, enabling better management of machine learning models.

• In 2023, IBM launched an upgraded version of its Watson Studio platform, enhancing collaboration tools and integrating more robust MLOps features for improved model deployment and monitoring.