Machine Learning Market Size, Share, Growth Analysis, By Component, By Deployment, By Enterprise Size, By End User, By Region - Industry Forecast 2024-2031

Global Machine learning market size was valued at USD 35.80 billion in 2022 and is poised to grow from USD 48.04 billion in 2023 to USD 505.42 billion by 2031, growing at a CAGR of 34.20% in the forecast period (2024-2031).

Machine learning (ML), a subset of artificial intelligence (AI), enables software to predict outcomes with greater accuracy without explicit instructions by analyzing historical data to forecast future results. The global machine learning market is anticipated to grow as ML becomes increasingly significant in security analytics, particularly as businesses adopt more advanced security frameworks. The sheer volume of data generated and transmitted across networks challenges cyber specialists in detecting and analyzing potential threats. For example, IBM and the All-England Lawn Tennis Club have enhanced the Wimbledon experience by leveraging AI on IBM Cloud and hybrid cloud technologies, creating new digital features such as IBM Power Index with Watson and IBM Match Insights with Watson, which enrich fans' engagement with the tournament. With cyber risks becoming more complex and supply chain attacks rising by 42% in the US, impacting up to 7 million people, machine learning algorithms offer crucial support in anticipating, detecting, and addressing cyber threats. AT&T and IBM showcase the potential of 5G and edge computing through virtual environments, highlighting how businesses can leverage AT&T's connectivity and IBM's hybrid cloud and AI technologies to address current challenges, including supply chain issues, cyber threats, ransomware, and the demand for seamless services in the 5G era. This collaboration is expected to drive growth in the global machine learning market.

Top-down and bottom-up approaches were used to estimate and validate the size of the Global Machine Learning market and to estimate the size of various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.

Global Machine Learning Market Segmental Analysis

Global machine learning market is segmented based on the component, enterprise size, deployment, end-user, and region. Based on the component, the market is segmented into solutions, and services (professional services, and managed services). Based on organization size, the market is segmented into SMEs and large enterprises. Based on deployment mode, the market is segmented into cloud and on-premise. Based on vertical, the market is segmented into BFSI (fraud and risk management, customer segmentation, sales and marketing campaign management, investment prediction, digital assistance, others), healthcare and life sciences (disease identification and diagnosis, image analytics, personalized treatment, drug discovery/manufacturing, others), retail (inventory planning, recommendation engines, upsells and cross channel marketing, segmentation and targeting, others), telecommunications (customer analytics, network security, network optimization, others), government and defense (autonomous defense system, threat intelligence, others), manufacturing (predictive maintenance, revenue estimation, demand forecasting, supply chain management, others), energy and utilities (power/energy usage analytics, seismic data processing, carbon emission, smart grid management, others), automotive & transportation, advertising & media, others. Based on region, the market is segmented into North America, Europe, Asia-Pacific, Latin America, and MEA.

Drivers of the Global Machine Learning Market

Computer vision, an advanced technique integrating machine learning (ML) and deep learning, is exemplified by initiatives like Microsoft's InnerEye, which focuses on image analysis for diagnostic purposes. An example of this technology's potential is an AI model developed by researchers from IBM and Pfizer, which can predict with 71% accuracy the future onset of Alzheimer's disease in healthy individuals. This prediction is based on small samples of linguistic data collected through clinical verbal cognition tests.

Restraints in the Global Machine Learning Market

The growth of the machine learning market is driven by the numerous benefits offered by ML platforms. However, certain critical shortcomings are expected to hinder market expansion. One major constraint is the presence of inaccurate or underdeveloped algorithms, which can impact the precision needed for industrial applications that rely on big data and machine learning. High accuracy is essential, and until systems are fully optimized with minimal error margins, human intervention remains necessary. This need for human input and the potential for algorithmic inaccuracies can limit the overall growth of the global machine learning market.

Market Trends of the Global Machine Learning Market

Machine learning is poised to become a key trend in security analytics as businesses adopt increasingly advanced security frameworks. With the vast amounts of data generated and transmitted across networks, cyber specialists face significant challenges in detecting and analyzing potential threats. As cyber risks become more pervasive and complex, machine learning algorithms play a crucial role in helping enterprises and security teams swiftly anticipate, detect, and identify cyber-attacks. Consequently, incorporating advanced learning capabilities into analytics-driven solutions is becoming increasingly vital.