Machine Learning Operations Global Market Report 2024

The machine learning operations market size has grown exponentially in recent years. It will grow from $1.56 billion in 2023 to $2.16 billion in 2024 at a compound annual growth rate (CAGR) of 38.4%. The growth observed in the historic period can be attributed to several factors, including the increasing complexity of machine learning models, the rapid evolution of edge computing, the rising adoption of federated learning, the continuous integration of DevOps and MLOps practices, and a surge in the adoption of automated machine learning (AutoML). These trends collectively contributed to the development and expansion of Machine Learning Operations during that period.

The machine learning operations market size is expected to see exponential growth in the next few years. It will grow to $7.85 billion in 2028 at a compound annual growth rate (CAGR) of 38.1%. The anticipated growth in the forecast period can be attributed to the rise of cloud computing, increased adoption of machine learning across various industries, the development of model deployment technologies, the adoption of agile development practices, and the increased complexity of machine learning models. Major trends expected in the forecast period include the integration of augmented analytics, the democratization of machine learning, exponential growth in edge AI applications, automated hyperparameter tuning, and the enhancement of security in MLOps pipelines. These trends collectively shape the evolving landscape of Machine Learning Operations.

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.

Key players in the machine learning operations market are focusing on developing innovative solutions, such as managed machine learning platforms, to gain a competitive advantage. A managed machine learning platform is a comprehensive and integrated software solution that assists organizations in developing, deploying, and managing machine learning (ML) models without the need for users to handle the complexities of underlying infrastructure. Google LLC, a US-based technology company, exemplifies this trend with the launch of Vertex AI in May 2021. Vertex AI simplifies the deployment and maintenance of AI models, requiring fewer lines of code for training compared to other solutions. It integrates various Google Cloud services under a unified interface, facilitating a smooth transition from model experimentation to production. With MLOps features, Vertex AI enhances experimentation, feature management, and model deployment, catering to data scientists of all skill levels and offering an efficient solution for managing the end-to-end machine learning workflow.

In June 2021, Hewlett Packard Enterprise, a US-based information technology company, strategically acquired Determined.AI Inc. for an undisclosed amount. This acquisition strengthens HPE's capabilities in the machine learning domain, enabling the integration of Determined AI's powerful open-source platform into HPE's AI and high-performance computing offerings. The move empowers ML engineers to efficiently train models and extract faster and more accurate insights across various industries. Determined.AI Inc., a US-based software company, is recognized for providing an open-source machine learning platform.

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 2023. 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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