Telecom-Technology Desk

Machine Learning Market Size, Share, CAGR, Production, Consumption and Forecast 2025

Press release   •   Jan 13, 2020 22:23 EST

The global machine learning market size is expected to reach USD 96.7 billion by 2025 and anticipated to expand at a CAGR of 43.8% from 2019 to 2025. Production of massive amounts of data has increased the adoption of technologies that can provide a smart analysis of that data.

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Key suggestions from the report:

  • The emergence of connected AI is expected to enable ML algorithms to learn continuously based on newly available information. Such developments are anticipated to drive the market in the coming years
  • The advertising and media sector accounted for the largest share in 2018 owing to capabilities such as buyer's optimization, data processing, and analysis provided by the technology
  • H2O.ai announced a partnership with IBM Corporation, a multinational IT company, in June 2018. Through this partnership, H2O.ai will offer its GPU-powered machine learning, next-generation AI platform, and best-of-breed deep learning on IBM's Power Systems platform
  • Key players in the machine learning market include Amazon Web Services, Inc.; Baidu Inc.; Google Inc.; H2O.ai; Hewlett Packard Enterprise Development LP; Intel Corporation; International Business Machines Corporation; Microsoft Corporation; SAS Institute Inc.; and SAP SE.

Technologies such as Machine Learning (ML) are being rapidly adopted across various applications in order to automatically detect meaningful patterns within a data set. Software based on ML algorithms, such as search engines, anti-spam software, and fraud detection software, are being increasingly used, thereby contributing to market growth.

The rapid emergence of ML technology has increased its adoption across various application areas. It provides cloud computing optimization along with intelligent voice assistance. In healthcare, it is used for the diagnosis of individuals. In case of businesses, the use of ML models that are open source and have a standards-based structure has increased in recent years. These models can be easily deployed in various business programs and can help companies bridge the skills gap between IT programmers and information scientists.

Developments such as fine-tuned personalization, hyper-targeting, searching engine optimization, no-code environment, self-learning bots, and others are projected to change the machine learning landscape. The development of capsule network has replaced neural networks in order to provide more accuracy in pattern detection, with fewer errors. These advanced developments are anticipated to proliferate market growth in the foreseeable future.

Component Insights

Based on component, the market is divided into hardware, software, and services. The hardware segment is expected to register the highest CAGR over the forecast period. This can be attributed to growing adoption of hardware optimized for machine learning. Development of customized silicon chips with AI and ML capabilities is driving the adoption of hardware. Development of more powerful processing devices by companies such as SambaNova Systems are anticipated to further drive the market.

The software segment is expected to account for a moderate share in the market. The adoption of cloud-based software is anticipated to rise due to enhanced cloud infrastructure and hosting parameters. Cloud-based software allows users to move from machine to deep learning, thereby driving adoption. Demand for machine learning services has been on a rise in recent years. Managed services help customers manage their ML tools and deal with varied dependency stacks.

Enterprise Size Insights

Based on enterprise size, the machine learning market is categorized into Small and Medium Enterprises (SMEs) and large enterprises. The large enterprise segment accounted for the leading share in the market in 2018. This is due to increasing adoption of technologies such as artificial intelligence and data science to inject predictive insights into business operations. Large organizations are focusing on harnessing deep learning, machine learning, and optimization of decisions in order to deliver high business value.

The adoption of machine learning is rapidly increasing among small and medium-sized enterprises. This is owing to easy and cost-effective deployment offered by machine learning. Availability of deployment options such as on cloud, on-premise, or hybrid allows SMEs to easily scale up their growing pilot projects and artificial intelligence initiatives, eliminating the need for large up-front investments.

End-use Insights

Based on end use, the market is categorized into BFSI, healthcare, retail, law, advertising and media, agriculture, manufacturing, automotive and transportation, and others. While advertising and media held the leading share in 2018, the healthcare sector is expected to surpass this segment to account for the largest share by the end of the forecast period. This is due to rising adoption of this technology in emerging healthcare areas. For instance, this technology is being used to predict the probability of death of a person. Use of machine learning for quantitative insights for better diagnosis and using it to prevent diseases is moving the field of medicine from reactive to proactive and this is poised to drive the market.

The law segment is expected to register the highest CAGR over the forecast period. This is due to rising adoption of machine learning algorithms across various legal applications. In case of litigation, ML is used for continuous active learning for the process of document review. Due diligence analysis in the merger and acquisition process is done using ML. Privacy, information governance, expert systems, and client collaboration are some of the emerging legal areas that are adopting machine learning.

Regional Insights

The market in North America held the dominant share in 2018, thanks to numerous banking organizations in the region investing in ML-based firms. For instance, in November 2019, JPMorgan Chase & Co. announced its investment in Limeglass, a provider of AI, ML, and NLP to analyze institutional research. The latter company is expected to assist emerging technology companies in developing various products required for banking.

Asia Pacific is anticipated to register the highest CAGR over the forecast period. This is due to growing adoption of machine learning in emerging markets with a massive talent base, such as India. Greater access to consumers who are willing to try AI-enabled services and products is further driving the regional market. In May 2018, NITI Aayog, a policy think tank of the Government of India, collaborated with Google LLC, a multinational technology company. Through this collaboration, the former company will incubate and train start-ups based on AI in India.

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Segmentation:

  • Machine Learning Component Outlook (Revenue, USD Million, 2014 - 2025)
    • Hardware
    • Software
    • Service
  • Machine Learning Enterprise Size Outlook (Revenue, USD Million, 2014 - 2025)
    • SMEs
    • Large Enterprises
  • Machine Learning End-use Outlook (Revenue, USD Million, 2014 - 2025)
    • Healthcare
    • BFSI
    • Law
    • Retail
    • Advertising & Media
    • Automotive & Transportation
    • Agriculture
    • Manufacturing
    • Others
  • Machine Learning Regional Outlook (Revenue, USD Million, 2014 - 2025)
    • North America
      • U.S.
      • Canada
      • Mexico
    • Europe
      • Germany
      • U.K.
      • France
    • Asia Pacific
      • China
      • Japan
      • India
    • South America
      • Brazil
    • Middle East and Africa

    Table of Contents

    Chapter 1 Methodology and Scope
    1.1 Market Segmentation & Scope
    1.2 Market Definition
    1.3 Information Procurement
    1.4 Information Analysis
    1.5 Market formulation & data visualization
    1.5.1 Secondary sources & third-party perspectives
    1.5.2 Primary research
    1.6 Research Scope & Assumptions
    Chapter 2 Executive Summary
    2.1 Market Outlook
    Chapter 3 Machine Learning Market Variables, Trends & Scope
    3.1 Penetration & Growth Prospect Mapping
    3.1.1 Market Growth Prospects
    3.1.2 Market Penetration Mapping
    3.2 Industry Value Chain Analysis
    3.3 Market Dynamics
    3.3.1 Market driver analysis
    3.3.1.1 Increasing applications of ML algortihm
    3.3.1.2 Rising adoption of advanced technologies
    3.4 Market restraint analysis
    3.4.1 Lack of skilled labor
    3.5 Machine Learning Market Analysis Tools
    3.5.1 Industry Analysis - Porter’s
    3.5.1.1 Supplier Power
    3.5.1.2 Buyer Power
    3.5.1.3 Substitution Threat
    3.5.1.4 Threat from new entrant
    3.5.1.5 Competitive rivalry
    3.5.2 PEST Analysis
    3.5.2.1 Political Landscape
    3.5.2.2 Economic Landscape
    3.5.2.3 Social Landscape
    3.5.2.4 Technology Landscape
    3.5.3 Major Deals & Strategic Alliances Analysis
    Chapter 4 Machine Learning Market: Component Movement Analysis
    4.1 Machine Learning Market: Component Movement Analysis
    4.1.1 Hardware
    4.1.1.1 Global Market Estimates and Forecasts, from 2014 to 2025 (USD Million)
    4.1.2 Software
    4.1.2.1 Global Market Estimates and Forecasts, from 2014 to 2025 (USD Million)
    4.1.3 Service
    4.1.3.1 Global Market Estimates and Forecasts, from 2014 to 2025 (USD Million)
    Chapter 5 Machine Learning Market: Enterprise Size Movement Analysis
    5.1 Machine Learning Market: Enterprise Size Movement Analysis
    5.1.1 SMEs
    5.1.1.1 Global Market Estimates and Forecasts, from 2014 to 2025 (USD Million)
    5.1.2 Large Enterprises
    5.1.2.1 Global Market Estimates and Forecasts, from 2014 to 2025 (USD Million)
    Chapter 6 Machine Learning: End-Use Estimates and Trend Analysis
    6.1 Machine Learning Market: End-Use Movement Analysis
    6.1.1 Healthcare
    6.1.1.1 Global Market Estimates and Forecasts, from 2014 to 2025 (USD Million)
    6.1.2 BFSI
    6.1.2.1 Global Market Estimates and Forecasts, from 2014 to 2025 (USD Million)
    6.1.3 Law
    6.1.3.1 Global Market Estimates and Forecasts, from 2014 to 2025 (USD Million)
    6.1.4 Retail
    6.1.4.1 Global Market Estimates and Forecasts, from 2014 to 2025 (USD Million)
    6.1.5 Advertising & Media
    6.1.5.1 Global Market Estimates and Forecasts, from 2014 to 2025 (USD Million)
    6.1.6 Automotive & Transportation
    6.1.6.1 Global Market Estimates and Forecasts, from 2014 to 2025 (USD Million)
    6.1.7 Agriculture
    6.1.7.1 Global Market Estimates and Forecasts, from 2014 to 2025 (USD Million)
    6.1.8 Manufacturing
    6.1.8.1 Global Market Estimates and Forecasts, from 2014 to 2025 (USD Million)
    6.1.9 Others
    6.1.9.1 Global Market Estimates and Forecasts, from 2014 to 2025 (USD Million)
    Chapter 7 Machine Learning: Regional Estimates & Trend Analysis
    7.1 North America
    7.1.1 North America machine learning market, 2014 - 2025
    7.1.2 U.S.
    7.1.2.1 Market Estimates and Forecasts, by component from 2014 to 2025 (USD Million)
    7.1.2.2 Market Estimates and Forecasts, by enterprise size from 2014 to 2025 (USD Million)
    7.1.2.3 Market Estimates and Forecasts, by end-use from 2014 to 2025 (USD Million)
    7.1.3 Canada
    7.1.3.1 Market Estimates and Forecasts, by component from 2014 to 2025 (USD Million)
    7.1.3.2 Market Estimates and Forecasts, by enterprise size from 2014 to 2025 (USD Million)
    7.1.3.3 Market Estimates and Forecasts, by end-use from 2014 to 2025 (USD Million)
    7.1.4 Mexico
    7.1.4.1 Market Estimates and Forecasts, by component from 2014 to 2025 (USD Million)
    7.1.4.2 Market Estimates and Forecasts, by enterprise size from 2014 to 2025 (USD Million)
    7.1.4.3 Market Estimates and Forecasts, by end-use from 2014 to 2025 (USD Million)
    7.2 Europe
    7.2.1 Europe machine learning market, 2014 - 2025
    7.2.2 U.K.
    7.2.2.1 Market Estimates and Forecasts, by component from 2014 to 2025 (USD Million)
    7.2.2.2 Market Estimates and Forecasts, by enterprise size from 2014 to 2025 (USD Million)
    7.2.2.3 Market Estimates and Forecasts, by end-use from 2014 to 2025 (USD Million)
    7.2.3 Germany
    7.2.3.1 Market Estimates and Forecasts, by component from 2014 to 2025 (USD Million)
    7.2.3.2 Market Estimates and Forecasts, by enterprise size from 2014 to 2025 (USD Million)
    7.2.3.3 Market Estimates and Forecasts, by end-use from 2014 to 2025 (USD Million)
    7.2.4 France
    7.2.4.1 Market Estimates and Forecasts, by component from 2014 to 2025 (USD Million)
    7.2.4.2 Market Estimates and Forecasts, by enterprise size from 2014 to 2025 (USD Million)
    7.2.4.3 Market Estimates and Forecasts, by end-use from 2014 to 2025 (USD Million)
    7.3 Asia Pacific
    7.3.1 Asia Pacific machine learning market, 2014 - 2025
    7.3.2 China
    7.3.2.1 Market Estimates and Forecasts, by component from 2014 to 2025 (USD Million)
    7.3.2.2 Market Estimates and Forecasts, by enterprise size from 2014 to 2025 (USD Million)
    7.3.2.3 Market Estimates and Forecasts, by end-use from 2014 to 2025 (USD Million)
    7.3.3 Japan
    7.3.3.1 Market Estimates and Forecasts, by component from 2014 to 2025 (USD Million)
    7.3.3.2 Market Estimates and Forecasts, by enterprise size from 2014 to 2025 (USD Million)
    7.3.3.3 Market Estimates and Forecasts, by end-use from 2014 to 2025 (USD Million)
    7.3.4 India
    7.3.4.1 Market Estimates and Forecasts, by component from 2014 to 2025 (USD Million)
    7.3.4.2 Market Estimates and Forecasts, by enterprise size from 2014 to 2025 (USD Million)
    7.3.4.3 Market Estimates and Forecasts, by end-use from 2014 to 2025 (USD Million)
    7.4 South America
    7.4.1 South America machine learning market, 2014 - 2025
    7.4.2 Brazil
    7.4.2.1 Market Estimates and Forecasts, by component from 2014 to 2025 (USD Million)
    7.4.2.2 Market Estimates and Forecasts, by enterprise size from 2014 to 2025 (USD Million)
    7.4.2.3 Market Estimates and Forecasts, by end-use from 2014 to 2025 (USD Million)
    7.5 MEA
    7.5.1 MEA machine learning market, 2014 - 2025
    Chapter 8 Competitive Landscape
    8.1 Amazon Web Services, Inc.
    8.1.1 Company overview
    8.1.2 Financial performance
    8.1.3 Product benchmarking
    8.1.4 Recent developments
    8.2 Baidu Inc.
    8.2.1 Company overview
    8.2.2 Financial performance
    8.2.3 Product benchmarking
    8.2.4 Recent developments
    8.3 Google Inc.
    8.3.1 Company overview
    8.3.2 Financial performance
    8.3.3 Product benchmarking
    8.3.4 Recent developments
    8.4 H2o.AI
    8.4.1 Company overview
    8.4.2 Financial performance
    8.4.3 Product benchmarking
    8.4.4 Recent developments
    8.5 Hewlett Packard Enterprise Development LP
    8.5.1 Company overview
    8.5.2 Financial performance
    8.5.3 Product benchmarking
    8.5.4 Recent developments
    8.6 Intel Corporation
    8.6.1 Company overview
    8.6.2 Financial performance
    8.6.3 Product benchmarking
    8.6.4 Recent developments
    8.7 International Business Machines Corporation
    8.7.1 Company overview
    8.7.2 Financial performance
    8.7.3 Product benchmarking
    8.7.4 Recent developments
    8.8 Microsoft Corporation
    8.8.1 Company overview
    8.8.2 Financial performance
    8.8.3 Product benchmarking
    8.8.4 Recent developments
    8.9 SAS Institute Inc.
    8.9.1 Company overview
    8.9.2 Financial performance
    8.9.3 Product benchmarking
    8.9.4 Recent developments
    8.10 SAP SE
    8.10.1 Company overview
    8.10.2 Financial performance
    8.10.3 Product benchmarking
    8.10.4 Recent developments

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