What is the current size and annual growth rate of the machine learning development market?
The machine learning development market size has grown exponentially in recent years. It will grow from $52.23 billion in 2024 to $74.16 billion in 2025 at a compound annual growth rate (CAGR) of 42.0%. The growth in the historic period can be attributed to increase the pace of diagnosis, increase the accuracy of machine learning, increase in demand for machine learning services, increase in data volume, and increasing internet penetration.
The machine learning development market size is expected to see exponential growth in the next few years. It will grow to $298.06 billion in 2029 at a compound annual growth rate (CAGR) of 41.6%. The growth in the forecast period can be attributed to growing infrastructure support, rising demand for automation, increase in data volume, increasing demand for cloud-based solutions, rising need for personalized solutions, and growing awareness of the benefits of machine learning. Major trends in the forecast period include technological advancements, innovations in artificial intelligence (AI) technologies, no-code and low-code technologies, foundation models and multimodal learning, and automated machine learning (AutoML).
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Which major factors have contributed to the expansion of the machine learning development market?
The increasing demand for cloud-based solutions is expected to propel the growth of the machine learning development market going forward. Cloud-based solutions refer to services, applications, and resources hosted and delivered over the Internet rather than on local servers or personal devices. The increasing use of cloud-based solutions is due to security and compliance, remote access to applications, highly redundant and resilient infrastructure, and prioritized security and compliance. Machine learning enhances cloud-based solutions by enabling smarter data analysis, automation, and personalized user experiences. It allows for scalable, real-time insights and can optimize resource allocation, ultimately improving efficiency and decision-making. For instance, in December 2023, according to Eurostat, a Luxembourg-based official website of the European Union, cloud-based solutions experienced a 4.2% increase in adoption throughout 2023, with 45.2% of businesses using cloud computing services, a significant rise from 2021. Therefore, increasing demand for cloud-based solutions is driving the growth of the machine learning development market.
How is the machine learning development market segmented?
The machine learning development market covered in this report is segmented –
1) By Deployment Mode: On-Premises; Cloud-Based
2) By Application: Predictive Maintenance; Fraud Detection and Prevention; Customer Segmentation; Image and Speech Recognition; Other Applications
3) By End-User: Healthcare; Finance; Retail; Manufacturing; Information Technology And Telecom; Other End-Users
Subsegments:
1) By On-Premises: On-Premises Machine Learning Development Platforms; On-Premises Data Storage for Machine Learning; On-Premises Machine Learning Model Training; On-Premises AI/ML Development Tools; On-Premises Machine Learning Frameworks
2) By Cloud-Based: Cloud-Based Machine Learning Development Platforms; Cloud-Based Data Storage and Processing for Machine Learning; Cloud-Based Model Training and Deployment; Cloud-Based AI/ML Development Services; Cloud-Based Machine Learning Frameworks and Libraries
3) By Managed Cloud ML Solutions
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Who are the top competitors in the machine learning development market?
Major companies operating in the machine learning development market areAlphabet Inc., Microsoft Corporation, Mitsubishi Electric Corporation, Robert Bosch GmbH, Tencent Holdings Ltd., Amazon Web Services (AWS) Inc., Intel Corporation, Siemens AG, General Electric (GE), International Business Machines (IBM) Corporation, Schneider Electric SE, Honeywell International Inc., NVIDIA Corporation, Rockwell Automation, Fanuc, Dassault Systemes SA, Hexagon AB, Autodesk Inc., PTC Inc., Emerson Electric Co., Yokogawa Electric Corporation
Which key trends are expected to influence the machine learning development market in the coming years?
Major companies operating in the machine learning development market are focused on developing innovative technology such as integrated development environment (IDE) to enhance the efficiency and scalability of their machine learning workflows. For machine learning development, an Integrated Development Environment (IDE) provides tools and features tailored to the needs of data scientists and machine learning engineers. These IDEs support the entire machine learning workflow, from data preprocessing to model training and evaluation. For instance, in September 2023, Microchip Technology Inc., a US-based semiconductor company, launched MPLAB, a Machine Learning Development Suite. The suite provides a complete and streamlined workflow for ML model development, enabling engineers to add ML inference capabilities to their products quickly. It is particularly beneficial for embedded systems where efficiency, security, and power consumption are critical.
Which regional trends are influencing the machine learning development market, and which area dominates the industry?
North America was the largest region in the machine learning development market in 2024.Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the machine learning development market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
What Does The Machine Learning Development Market Report 2025 Offer?
The machine learning development market research report from The Business Research Company offers global market size, growth rate, regional shares, competitor analysis, detailed segments, trends, and opportunities.
Machine learning development refers to the process of creating and refining algorithms and models that enable computers to learn from data and make decisions or predictions without being explicitly programmed for specific tasks. It combines data science, programming, and domain knowledge expertise to create systems that can learn and adapt over time, providing valuable insights and automation capabilities.
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