Machine Learning Operations Market Report 2025

This report covers market characteristics, size and growth, segmentation, regional and country breakdowns, competitive landscape, market shares, trends and strategies for this market. It traces the market’s historic and forecast market growth by geography.

The machine learning operations market size has grown exponentially in recent years. It will grow from $2.16 billion in 2024 to $2.99 billion in 2025 at a compound annual growth rate (CAGR) of 38.4%. The growth in the historic period can be attributed to increasing complexity of ml models, rapid evolution of edge computing, increasing adoption of federated learning, continuous integration of devops and mlops, surge in automl adoption.

The machine learning operations market size is expected to see exponential growth in the next few years. It will grow to $10.84 billion in 2029 at a compound annual growth rate (CAGR) of 38%. The growth in the forecast period can be attributed to rise of cloud computing, increased adoption of machine learning in industries, development of model deployment technologies, adoption of agile development practices, increased complexity of machine learning models. Major trends in the forecast period include augmented analytics integration, democratization of machine learning, exponential growth in edge ai applications, automated hyperparameter tuning, enhanced security in mlops pipelines.

The increasing demand for self-driving cars is poised to drive the growth of the machine-learning operations (MLOps) market. Self-driving cars are equipped with advanced sensors, cameras, radar, lidar, and artificial intelligence (AI) systems that enable them to navigate and make decisions on the road without direct human intervention. MLOps in self-driving cars involves the continuous integration, deployment, and management of machine learning models within the vehicles. This allows them to adapt and improve their driving capabilities based on real-time data from sensors and diverse driving scenarios. According to a report from the Insurance Institute for Highway Safety in December 2022, an estimated 3.5 million autonomous vehicles are projected to be on American roads by 2025, with expectations for this number to increase to 4.5 million by 2030. The surging demand for self-driving cars is identified as a significant driver of the machine-learning operations market.

Major companies in the machine learning operations (MLOps) market are introducing innovative solutions such as GPT Monitoring for MLOps, which allows for real-time monitoring and cost tracking of GPT models, enhancing performance and operational efficiency for engineering teams. GPT Monitoring for MLOps leverages generative pre-trained transformers to improve the tracking and management of machine learning operations, enabling better model performance and decision-making. For example, in March 2023, New Relic, a U.S.-based digital intelligence company, launched New Relic Machine Learning Operations (MLOps) for real-time monitoring of applications using OpenAI's GPT series APIs. This new feature enables engineering teams to monitor performance and costs with just two lines of code, offering immediate insights into GPT usage. It supports all versions of OpenAI GPT, helping companies optimize AI-driven applications while reducing operational costs.

In March 2024, Bain & Company, a U.S.-based management consulting services firm, acquired PiperLab for an undisclosed amount. This acquisition aims to bolster Bain's artificial intelligence (AI) and machine learning (ML) capabilities across Europe, the Middle East, and Africa (EMEA). By integrating PiperLab’s expertise and solutions, Bain plans to create an additional hub within its global Advanced Analytics Group (AAG), enabling a unified team to address complex business challenges at the intersection of business, data science, and engineering. PiperLab, a Spain-based company, specializes in providing data-driven solutions that focus on enhancing operational efficiency, increasing productivity, and reducing costs for businesses.

Major companies operating in the machine learning operations market report are Amazon.com Inc., Alphabet Inc., Microsoft Corporation, International Business Machines Corporation, Hewlett Packard Enterprise, Statistical Analysis System (SAS), Databricks Inc., Cloudera Inc., Alteryx Inc., Comet, GAVS Technologies, DataRobot Inc., Veritone, Dataiku, Parallel LLC, Neptune Labs, SparkCognition, Weights & Biases, Kensho Technologies Inc., Akira.Al, Iguazio, Domino Data Lab, Symphony Solutions, Valohai, Blaize, Neptune.ai, H2O.ai, Paperspace, OctoML.

North America was the largest region in the machine learning operations market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the machine learning operations market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the machine learning operations market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

The machine learning operations market includes revenues earned by entities by providing services including model deployment services, integration services, data management services, cloud services and testing services. The market value includes the value of related goods sold by the service provider or included within the service offering. Only goods and services traded between entities or sold to end consumers are included. The machine learning operations market consists of sales of central processing units (CPUs), graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and tensor processing units (TPUs). Values in this market are ‘factory gate’ values, that is the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD, unless otherwise specified).

Machine Learning Operations, often referred to as MLOps, encompasses a set of practices and tools designed to automate and manage the complete lifecycle of machine learning models, starting from their development and training phases. MLOps involves a range of tasks related to deploying, managing, and monitoring machine learning models in production environments. It aims to streamline and enhance the efficiency of the operational aspects associated with the deployment and ongoing maintenance of machine learning solutions.

The primary types of deployments in Machine Learning Operations (MLOps) include on-premise, cloud, and other variations. On-premise deployment involves installing and running software or systems within an organization's physical infrastructure or data centers. This deployment method caters to enterprises of various sizes, including large enterprises and small to medium-sized enterprises. On-premise MLOps finds applications across diverse industry sectors such as banking, financial services, and insurance (BFSI), manufacturing, IT and telecom, retail, and e-commerce, energy and utility, healthcare, media and entertainment, among others.

The machine learning operations market research report is one of a series of new reports that provides machine learning operations market statistics, including machine learning operations industry global market size, regional shares, competitors with machine learning operations market share, detailed machine learning operations market segments, market trends, and opportunities, and any further data you may need to thrive in the machine learning operations industry. This machine learning operations market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenarios of the industry.

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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