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Machine Learning - Thematic Intelligence

Best Price Guarantee Length Publisher Published Date SKU
from $1,950 84 Pages GlobalData December, 2022 GBDT17577164
Best Price Guarantee
Price from $1,950
Length 84 Pages
Publisher GlobalData
Published Date December, 2022
SKU GBDT17577164

Machine Learning - Thematic Intelligence

Summary

Machine learning is a subset of artificial intelligence (AI) that allows computer systems to learn and improve from data without being explicitly programmed. Machine learning is the most practical application of AI currently available for enterprise adoption.

Key Highlights

  • GlobalData forecasts the global AI market will be worth $136 billion in 2026. Specialist AI applications will account for the largest proportion of 2026 revenue at 37%, followed by AI consulting and support services at 30%. AI platforms will record the fastest revenue growth between 2021 and 2026 (a CAGR of 18%).
  • Instead of companies employing programmers to design machine learning tools from scratch, nocode/low-code and machine learning as a service (MLaaS) platforms allow those without extensive programming ability to design systems tailored to their needs. This has also led to the popularity of machine learning operations (MLOps) to ensure systems are implemented and monitored to a high standard.
  • AI is increasingly involved in life-changing decisions like welfare payments, mortgage approvals, and medical diagnoses. Consequently, transparency and explainability have become essential. Some key AI frameworks driving transparency in the sector include IBM’s open-source AI 360 tool kit and Rulex’s Logic Leaning Machine (LLM).
  • The main areas driving AI M&A deals are NLP, automated driving, MLaaS, and enterprise predictive analytics.
  • US-based machine learning companies have raised a total of $57,960 million through 3,038 venture financing deals in the last 10 years.
Scope
  • This report provides an overview of the machine learning theme.
  • It identifies the key trends impacting growth of the theme over the next 12 to 24 months.
  • It includes a comprehensive industry analysis, including market size and growth forecasts for AI hardware, AI platforms, AI consulting and support services, and specialized AI applications.
  • The detailed value chain breaks down machine learning into three areas: hardware, software (big data management and machine learning techniques), and services (platforms, MLaaS, and libraries).
Reasons to Buy
  • Machine learning will benefit all enterprises in some capacity, with potential advantages including automation, trend and pattern recognition, process improvement, and forecasting. This report will help readers make sense of the machine learning theme, understand training techniques and leading algorithms, the business benefits, identify the leading vendors and startups, and understand MLaaS, MLOps, and machine learning libraries.


  • Executive Summary
    • Table Figure 1: GlobalData estimates that the global AI market
  • Players
    • Hardware
      • Table Figure 2: Who are the leading players in machine learning hardware, and where do they sit in the value chain?
    • Services
      • Table Figure 3: Who are the leading players in machine learning services, and where do they sit in the value chain?
  • Technology Briefing
    • What is machine learning?
      • Table Figure 4: Machine learning is a subset of artificial intelligence
    • Machine learning training techniques
      • Table Figure 5: Machine learning can use supervised learning, unsupervised learning, or reinforcement learning
    • What are deep learning and neural networks?
      • Table Figure 6: Deep learning neural network structure
    • How are machine learning models trained, tested, and deployed?
      • Table Figure 7: Machine learning models are trained and tested using existing data before they can make predictions
      • Data collection
      • Data preparation
      • Training
      • Model evaluation
      • Parameter tuning
      • Publication and monitoring
    • Cloud, AI chips, and edge computing
    • How does machine learning affect industries?
      • Table Figure 8: Machine learning is impacting every industry
  • Trends
    • Table Figure 9: Machine learning trends
    • Technology trends
      • Table Technology trends
    • Macroeconomic trends
      • Table Macroeconomic trends
    • Regulatory trends
      • Table Regulatory trends
  • Industry Analysis
    • Market size and growth forecasts
      • Table Figure 10: The AI market is predicted to grow substantially from 2022 to 2026
      • Table Figure 11: The US dominates the AI market, while computer vision is the leading specialist AI application
    • Mergers and acquisitions
      • Table Mergers and acquisitions
    • Venture financing
      • Table Figure 12: After the US, China is the second largest country for machine learning-related venture finance
      • Table Venture financing
      • Table Figure 13: The total value of venture finance machine learning deals increased between 2020 and 2021
      • Foreign direct investment
        • Table Figure 14: Machine learning R&D operations top FDI business functions
    • Patent trends
      • Table Figure 15: Machine learning patents rose steadily throughout 2020 and 2021 but declined in Q2 2022
      • Table Figure 16: China dominates machine learning patent registrations, but US-based IBM has filed the most patents
    • Company filing trends
      • Table Figure 17: Machine learning is increasingly mentioned in company filings
      • Table Figure 18: Machine learning was mentioned by companies from various industries
    • Hiring trends
      • Table Figure 19: Amazon has consistently hired the most machine learning professionals
    • Use cases
      • Senseye develops machine learning for predictive maintenance in manufacturing
        • Table Figure 20: Senseye’s software tracks assets in real-time
      • Pony.ai uses machine learning in self-driving trucks
        • Table Figure 21: Pony.ai has developed a line of self-driving trucks
      • Autonomous weapons can target individuals
        • Table Figure 22: The Kargu-2 kamikaze drone
      • Machine learning in financial services
        • Table Figure 23: SEON suggests risk rules based on historical data
      • AlphaFold uses machine learning to predict protein structures
        • Table Figure 24: Some AlphaFold protein predictions and their significance
      • Veritone Energy’s demand management solution saves costs and energy for utilities
      • Machine learning helps consumers track their environmental impact
        • Table Figure 25: Evocco tracks the carbon footprint of your shopping
    • Timeline
      • Table Figure 26: The machine learning story
  • Value Chain
    • Table Figure 27: The machine learning value chain
    • Hardware
      • AI chips
        • Table Figure 28: The machine learning value chain - AI chips
      • Sensors
        • Table Figure 29: The machine learning value chain - sensors
      • Servers
        • Table Figure 30: The machine learning value chain - servers
      • Storage
        • Table Figure 31: The machine learning value chain - storage
      • Networking equipment
        • Table Figure 32: The machine learning value chain - networking equipment
    • Software
      • Big data management
        • Table Figure 33: The machine learning value chain - big data management
      • Machine learning techniques
        • Table Figure 34: The machine learning value chain - machine learning techniques
        • Table Figure 35: Classification predicts the outcome of data points into pre-set categories
        • Table Figure 36: Linear regression predicts continuous numerical values based on historical training data
        • Table Figure 37: The machine learning value chain - machine learning techniques
        • Table Figure 38: K-means clustering algorithms assign data points to a predetermined number of clusters based on similarities
        • Table Figure 39: The machine learning value chain - machine learning techniques
    • Services
      • Machine learning libraries
        • Table Machine learning libraries
        • Table Figure 40: The machine learning value chain - machine learning libraries
      • Machine learning platforms
        • Table Figure 41: The machine learning value chain - machine learning platforms
      • Machine learning as a service
        • Table Figure 42: The machine learning value chain - machine learning as a service
  • Companies
    • Public companies
      • Table Public companies
    • Private companies
      • Table Private companies
  • Sector Scorecards
    • Application software sector scorecard
      • Who’s who
        • Table Figure 43: Who does what in the application software space?
      • Thematic screen
        • Table Figure 44: Thematic screen - Application software sector scorecard
      • Valuation screen
        • Table Figure 45: Valuation screen - Application software sector scorecard
      • Risk screen
        • Table Figure 46: Risk screen - Application software sector scorecard
    • Cloud services sector scorecard
      • Who’s who
        • Table Figure 47: Who does what in the cloud services space?
      • Thematic screen
        • Table Figure 48: Thematic screen - Cloud services sector scorecard
      • Valuation screen
        • Table Figure 49: Valuation screen - Cloud services sector scorecard
      • Risk screen
        • Table Figure 50: Risk screen - Cloud services sector scorecard
  • Glossary
    • Table Glossary
  • Further Reading
    • GlobalData reports
      • Table GlobalData reports
  • Our thematic research methodology
    • Table Figure 51: Our five-step approach for generating a sector scorecard
  • About GlobalData
  • Contact Us

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