Machine Learning Operations Market Insights: Key Drivers, Trends, And Forecast 2024-2033

The Business Research Company has recently revised its global market reports, now incorporating the most current data for 2024 along with projections extending up to 2033.

Machine Learning Operations Global Market Report 2024 by The Business Research Company offers comprehensive market insights, empowering businesses with a competitive edge. It includes detailed estimates for numerous segments and sub-segments, providing valuable strategic guidance.

The Market Size Is Expected To Reach $7.85 billion In 2028 At A CAGR Of 38.1% :

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.

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Machine Learning Operations Market Major Segments
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

Key Driver - Self-Driving Car Demand Fuels Growth in the Machine Learning Operations Market

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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Prominent Trend - Innovations Transforming The Machine Learning Operations Market

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.

Machine Learning Operations Market Players
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

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Largest And Fastest Growing Region In The Market
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 Table Of Content For The Market Report Include:
1. Executive Summary
2. Machine Learning Operations Market Report Structure
3. Machine Learning Operations Market Trends And Strategies
4. Machine Learning Operations Market - Macro Economic Scenario
5. Machine Learning Operations Market Size And Growth
…..
27. Machine Learning Operations Market Competitor Landscape And Company Profiles
28. Key Mergers And Acquisitions
29. Future Outlook and Potential Analysis
30. Appendix

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