Machine Learning Operations Market Forecast 2024-2033: Growth Rate, Drivers, And Trends

The Machine Learning Operations Global Market Report 2024 by The Business Research Company provides market overview across 60+ geographies in the seven regions – Asia-Pacific, Western Europe, Eastern Europe, North America, South America, the Middle East, and Africa, encompassing 27 major global industries. The report presents a comprehensive analysis over a ten-year historic period (2010-2021) and extends its insights into a ten-year forecast period (2023-2033).

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According to The Business Research Company’s 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 in the historic period can be attributed to increasing complexity of ml models, rapid evolution of edge computing, increasing adoption of federated learning, ontinuous 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 $7.85 billion in 2028 at a compound annual growth rate (CAGR) of 38.1%.  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 rising demand for self-driving cars is expected to propel the growth of the machine-learning operations market going forward. Self-driving cars are automobiles equipped with advanced sensors, cameras, radar, lidar, and artificial intelligence (AI) systems that enable them to navigate, operate, and make decisions on the road without direct human intervention. Machine learning operations (MLOps) in self-driving cars involve the continuous integration, deployment, and management of machine learning models within the vehicles, enabling them to adapt and improve their driving capabilities based on real-time data from sensors and diverse driving scenarios. For instance, in December 2022, according to a report published by the Insurance Institute for Highway Safety, a US-based non-profit organization, it is projected that there will be an estimated 3.5 million autonomous vehicles on American roads by 2025, with expectations for this number to increase to 4.5 million by the year 2030. Therefore, the rising demand for self-driving cars is driving the growth of the machine-learning operations market.

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The machine learning operations market covered in this report is segmented –

1) By Deployment Type: On-premise, Cloud, Other Type Of Deployment
2) By Organization Size: Large Enterprises, Small and Medium-sized Enterprises
3) By Industry Vertical: BFSI (Banking, Financial Services, and Insurance), Manufacturing, IT and Telecom, Retail and E-commerce, Energy and Utility, Healthcare, Media and Entertainment, Other Industry Verticals

Major companies operating in the machine learning operations market are developing new innovative solutions, such as managed machine learning platforms, to gain a competitive edge in the market. Managed machine learning platform refers to a comprehensive and integrated software solution that assists organizations in developing, deploying, and managing machine learning (ML) models without requiring users to handle the underlying infrastructure complexities. For instance, in May 2021, Google LLC, a US-based technology company, launched Vertex AI, a managed machine learning platform. This platform simplifies the deployment and maintenance of AI models, requiring fewer lines of code for training compared to other solutions. Vertex AI integrates various Google Cloud services under a unified interface, accelerating the transition from model experimentation to production. It includes MLOps features to enhance experimentation, feature management, and model deployment. The platform is designed to be accessible to data scientists of all skill levels, offering a comprehensive solution for managing the end-to-end machine learning workflow efficiently.

The machine learning operations market report table of contents includes:

1. Executive Summary
2. Market Characteristics
3. Market Trends And Strategies
4. Impact Of COVID-19
5. Market Size And Growth
6. Segmentation
7. Regional And Country Analysis
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27. Competitive Landscape And Company Profiles
28. Key Mergers And Acquisitions
29. Future Outlook and Potential Analysis

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