자기 지도 학습 시장 규모, 점유율, 동향 분석 보고서 : 최종 용도별, 기술별, 지역별, 부문별 예측(2025-2030년)

Self-supervised Learning Market Size, Share & Trends Analysis Report By End Use (Healthcare, BFSI), By Technology (Natural Language Processing, Computer Vision), By Region, And Segment Forecasts, 2025 - 2030

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한글목차

자기 지도 학습 시장 성장과 동향:

Grand View Research, Inc의 최신 보고서에 따르면 전 세계 자기 지도 학습 시장 규모는 2030년까지 896억 8,000만 달러에 달할 것으로 예상되며, 2025년부터 2030년까지 35.2%의 CAGR로 확대될 것으로 예상됩니다.

자기 지도 학습은 자연어 처리(NLP)에서 두드러지게 사용되는 기계 학습 기술이며, 컴퓨터 비전 및 음성 처리 응용 프로그램이 그 뒤를 잇습니다. 자기 지도 학습의 응용 분야에는 말 바꾸기, 색상화, 음성 인식 등이 포함됩니다.

COVID-19 사태는 시장에 긍정적인 영향을 미쳤으며, 많은 기업들이 COVID-19에 대응하기 위해 AI와 머신러닝을 도입하고 있습니다. 미국에 본사를 둔 Amazon Web Services, Inc., Google, Microsoft 등 많은 유명 시장 기업들이 팬데믹 기간 동안 매출 증가를 목격했습니다. 또한, 디지털화의 가속화는 자기 지도 학습 애플리케이션의 채택에도 기여했습니다. 예를 들어, 2020년 4월 구글의 사업부인 구글 클라우드(Google Cloud)는 COVID-19 팬데믹에 대응하기 위해 중요한 정보를 제공하는 인공지능(AI) 챗봇을 발표했습니다.

많은 시장 기업들이 음성 합성, 언어 번역 및 예측 등 다양한 애플리케이션을 위한 솔루션을 제공하고 있습니다. 또한, 이들 업체들은 자기 지도 학습에 대한 연구도 진행하고 있습니다. 예를 들어, 미국에 본사를 둔 메타(Meta)는 교습 학습을 연구하고 다양한 알고리즘과 모델을 개발해 왔으며, 2022년 2월 메타는 자사의 자기 지도 학습 컴퓨터 비전 모델 SEER의 새로운 진전을 발표했습니다. 이 모델은 더욱 강력해져 컴퓨터 비전 제품을 구축할 수 있을 것으로 기대됩니다.

자기 지도 학습 시장 보고서 하이라이트

• 최종 용도별로는 BFSI 부문이 2024년 18.3%의 가장 큰 매출 점유율을 차지했으며, 예측 기간 동안에도 그 위치를 유지할 것으로 예상됩니다. 이는 이 부문에서 AI 및 ML과 같은 기술의 채택이 증가하고 있기 때문입니다. 광고 및 미디어 부문은 예측 기간 동안 유리한 성장을 기록할 것으로 예상됩니다.
• 기술별로는 자연어 처리 분야가 2024년 지배적인 점유율을 차지했는데, 이는 여러 산업에 걸쳐 방대한 양의 비정형 텍스트 데이터를 처리할 수 있는 능력 때문입니다. 이는 NLP 애플리케이션의 다양성과 보급에 기인합니다.
• 북미는 2024년 35.7%의 가장 큰 점유율을 차지했으며, 예측 기간 동안에도 그 위치를 유지할 것으로 예상됩니다. 이는 이 지역에 다수의 시장 기업이 존재하기 때문입니다. 또한, 전문 인력의 존재와 기술 인프라의 개발이 시장 성장을 촉진하고 있습니다.
• 2024년 7월, 구글 LLC는 인도에서 농업 경관 이해(ALU) 도구를 발표했습니다. 이 AI 기반 플랫폼은 고해상도 위성 이미지와 기계 학습을 사용하여 개별 농장 수준에서 가뭄 대책, 관개 및 작물 관리에 대한 심층적인 인사이트를 제공합니다.
• 2024년 5월, Meta AI, Google, INRIA, University Paris Saclay의 연구팀은 임베디드 모델과 계층적 k-means 클러스터링을 이용한 자기 지도 학습(SSL)을 위한 자동 데이터 세트 큐레이션 기술을 개발했습니다. 이 기술은 균형 잡힌 데이터 세트를 보장하고 수동 큐레이션과 관련된 비용과 시간을 줄여 모델 성능을 향상시킵니다.

목차

제1장 조사 방법과 범위

제2장 주요 요약

제3장 자기 지도 학습 시장 변수, 동향, 범위

• 세계의 자기 지도 학습 시장 전망
• 업계 밸류체인 분석
• 시장 역학
  • 시장 성장 촉진요인 분석
  • 시장 성장 억제요인 분석
  • 업계 과제
• Porter's Five Forces 분석
  • 공급업체의 능력
  • 구매자의 능력
  • 대체의 위협
  • 신규 참여업체의 위협
  • 경쟁 기업 간의 경쟁 관계
• PESTEL 분석
  • 정치 상황
  • 경제 상황
  • 사회 상황
  • 기술적 상황
  • 환경 상황
  • 법적인 상황

제4장 자기 지도 학습 시장 : 최종 용도 전망 추정과 예측

• 자기 지도 학습 시장 : 최종 용도 변동 분석, 2024년 및 2030년
  • 헬스케어
  • BFSI
  • 자동차·수송
  • 소프트웨어 개발(IT)
  • 광고와 미디어
  • 기타

제5장 자기 지도 학습 시장 : 기술 전망 추정과 예측

• 자기 지도 학습 시장 : 기술 변동 분석, 2024년 및 2030년
  • 자연어 처리(NLP)
  • 컴퓨터 비전
  • 음성 처리

제6장 자기 지도 학습 시장 : 지역 추정·동향 분석

• 자기 지도 학습 시장 점유율, 지역별, 2024년 및 2030년
• 북미
  • 미국
  • 캐나다
  • 멕시코
• 유럽
  • 영국
  • 독일
  • 프랑스
• 아시아태평양
  • 중국
  • 일본
  • 인도
  • 한국
  • 호주
• 라틴아메리카
  • 브라질
• 중동 및 아프리카
  • 아랍에미리트
  • 사우디아라비아
  • 남아프리카공화국

제7장 경쟁 상황

• 주요 시장 진출 기업의 최근 동향과 영향 분석
• 벤더 상황
  • 기업 분류
  • 주요 판매대리점 및 채널 파트너 리스트
  • 잠재 고객 리스트/리스트
• 경쟁 역학
  • 경쟁 벤치마킹
  • 전략 매핑
  • 히트맵 분석
• 기업 개요
  • Alphabet Inc.(Google LLC)
  • Amazon Web Services, Inc.
  • Apple Inc.
  • Baidu, Inc.
  • Iberdrola SA Dataiku
  • Databricks
  • DataRobot, Inc.
  • IBM Corporation
  • Meta
  • Microsoft
  • SAS Institute Inc.
  • Tesla
  • The MathWorks, Inc

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영문 목차

영문목차

Self-supervised Learning Market Growth & Trends:

The global self-supervised learning market size is estimated to reach USD 89.68 billion by 2030, expanding at a CAGR of 35.2% from 2025 to 2030, according to a new report by Grand View Research, Inc. Self-supervised learning is a machine learning technique used prominently in Natural Language Processing (NLP), followed by computer vision and speech processing applications. Applications of self-supervised learning include paraphrasing, colorization, and speech recognition.

The COVID-19 pandemic had a positive impact on the market. More businesses adopted AI and Machine Learning as a response to the COVID-19 pandemic. Many prominent market players such as U.S.-based Amazon Web Services, Inc., Google, and Microsoft witnessed a rise in revenue during the pandemic. Moreover, accelerated digitalization also contributed to the adoption of self-supervised learning applications. For instance, in April 2020, Google Cloud, a business segment of Google, launched an Artificial Intelligence (AI) chatbot that provides critical information to fight the COVID-19 pandemic.

Many market players offer solutions for various applications such as text-to-speech and language translation & prediction. Moreover, these players are researching in self-supervised learning. For instance, U.S.-based Meta has been advancing in self-supervised learning research and has developed various algorithms and models. In February 2022, Meta announced new advances in the company's self-supervised computer vision model SEER. The model is more powerful and is expected to enable the company in building computer vision products.

Self-supervised Learning Market Report Highlights:

• In terms of end-use, the BFSI segment accounted for the largest revenue share of 18.3% in 2024 and is expected to retain its position over the forecast period. This can be attributed to the increasing adoption of technologies such as AI and ML in the segment. The Advertising & Media segment is anticipated to register lucrative growth over the forecast period.
• Based on technology, the natural language processing segment accounted for the dominant share in 2024 due to its ability to handle vast amounts of unstructured text data across multiple industries.. This can be attributed to the variety and penetration of NLP applications.
• North America held the largest share of 35.7% in 2024 and is expected to retain its position over the forecast period. This can be attributed to the presence of a large number of market players in the region. Moreover, the presence of specialists and developed technology infrastructure are aiding the growth of the market.
• In July 2024, Google LLC launched the Agricultural Landscape Understanding (ALU) tool in India, an AI-based platform that uses high-resolution satellite imagery and machine learning to provide detailed insights on drought preparedness, irrigation, and crop management at an individual farm level.
• In May 2024, Researchers from Meta AI, Google, INRIA, and University Paris Saclay created an automatic dataset curation technique for self-supervised learning (SSL) using embedding models and hierarchical k-means clustering. This method improves model performance by ensuring balanced datasets and reducing the costs and time associated with manual curation.

Table of Contents

Chapter 1. Methodology and Scope

• 1.1. Market Segmentation & Scope
• 1.2. Market Definition
• 1.3. Information Procurement
  • 1.3.1. Purchased Database
  • 1.3.2. GVR's Internal Database
  • 1.3.3. Secondary Sources & Third-Party Perspectives
  • 1.3.4. Primary Research
• 1.4. Information Analysis
  • 1.4.1. Data Analysis Models
• 1.5. Market Formulation & Data Visualization
• 1.6. Data Validation & Publishing

Chapter 2. Executive Summary

• 2.1. Market Insights
• 2.2. Segmental Outlook
• 2.3. Competitive Outlook

Chapter 3. Self-supervised Learning Market Variables, Trends & Scope

• 3.1. Global Self-supervised Learning Market Outlook
• 3.2. Industry Value Chain Analysis
• 3.3. Market Dynamics
  • 3.3.1. Market Driver Analysis
  • 3.3.2. Market Restraint Analysis
  • 3.3.3. Industry Challenges
• 3.4. Porter's Five Forces Analysis
  • 3.4.1. Supplier Power
  • 3.4.2. Buyer Power
  • 3.4.3. Substitution Threat
  • 3.4.4. Threat from New Entrant
  • 3.4.5. Competitive Rivalry
• 3.5. PESTEL Analysis
  • 3.5.1. Political Landscape
  • 3.5.2. Economic Landscape
  • 3.5.3. Social Landscape
  • 3.5.4. Technological Landscape
  • 3.5.5. Environmental Landscape
  • 3.5.6. Legal Landscape

Chapter 4. Self-supervised Learning Market: End Use Outlook Estimates & Forecasts

• 4.1. Self-supervised Learning Market: End Use Movement Analysis, 2024 & 2030
  • 4.1.1. Healthcare
    • 4.1.1.1. Healthcare Market estimates and forecast, 2018 - 2030 (USD Million)
  • 4.1.2. BFSI
    • 4.1.2.1. BFSI Market estimates and forecast, 2018 - 2030 (USD Million)
  • 4.1.3. Automotive & Transportation
    • 4.1.3.1. Automotive & Transportation Market estimates and forecast, 2018 - 2030 (USD Million)
  • 4.1.4. Software Development (IT)
    • 4.1.4.1. Software Development (IT) Market estimates and forecast, 2018 - 2030 (USD Million)
  • 4.1.5. Advertising & Media
    • 4.1.5.1. Advertising & Media Market estimates and forecast, 2018 - 2030 (USD Million)
  • 4.1.6. Others
    • 4.1.6.1. Others Market estimates and forecast, 2018 - 2030 (USD Million)

Chapter 5. Self-supervised Learning Market: Technology Outlook Estimates & Forecasts

• 5.1. Self-supervised Learning Market: Technology Movement Analysis, 2024 & 2030
  • 5.1.1. Natural Language Processing (NLP)
    • 5.1.1.1. Natural Language Processing (NLP) Market estimates and forecast, 2018 - 2030 (USD Million)
  • 5.1.2. Computer Vision
    • 5.1.2.1. Computer Vision Market estimates and forecast, 2018 - 2030 (USD Million)
  • 5.1.3. Speech Processing
    • 5.1.3.1. Speech Processing Market estimates and forecast, 2018 - 2030 (USD Million)

Chapter 6. Self-supervised Learning Market: Regional Estimates & Trend Analysis

• 6.1. Self-supervised Learning Market Share, By Region, 2024 & 2030 (USD Million)
• 6.2. North America
  • 6.2.1. North America Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.2.1.1. North America Self-supervised Learning Market Estimates and Forecasts, by Country, 2018 - 2030 (USD Million)
    • 6.2.1.2. North America Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.2.1.3. North America Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
  • 6.2.2. U.S.
    • 6.2.2.1. U.S. Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.2.2.2. U.S. Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.2.2.3. U.S. Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
  • 6.2.3. Canada
    • 6.2.3.1. Canada Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.2.3.2. Canada Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.2.3.3. Canada Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
  • 6.2.4. Mexico
    • 6.2.4.1. Mexico Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.2.4.2. Mexico Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.2.4.3. Mexico Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
• 6.3. Europe
  • 6.3.1. Europe Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.3.1.1. Europe Self-supervised Learning Market Estimates and Forecasts, by Country, 2018 - 2030 (USD Million)
    • 6.3.1.2. Europe Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.3.1.3. Europe Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
  • 6.3.2. UK
    • 6.3.2.1. UK Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.3.2.2. UK Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.3.2.3. UK Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
  • 6.3.3. Germany
    • 6.3.3.1. Germany Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.3.3.2. Germany Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.3.3.3. Germany Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
  • 6.3.4. France
    • 6.3.4.1. France Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.3.4.2. France Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.3.4.3. France Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
• 6.4. Asia Pacific
  • 6.4.1. Asia Pacific Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.4.1.1. Asia Pacific Self-supervised Learning Market Estimates and Forecasts, by Country, 2018 - 2030 (USD Million)
    • 6.4.1.2. Asia Pacific Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.4.1.3. Asia Pacific Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
  • 6.4.2. China
    • 6.4.2.1. China Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.4.2.2. China Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.4.2.3. China Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
  • 6.4.3. Japan
    • 6.4.3.1. Japan Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.4.3.2. Japan Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.4.3.3. Japan Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
  • 6.4.4. India
    • 6.4.4.1. India Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.4.4.2. India Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.4.4.3. India Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
  • 6.4.5. South Korea
    • 6.4.5.1. South Korea Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.4.5.2. Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.4.5.3. South Korea Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
  • 6.4.6. Australia
    • 6.4.6.1. Australia Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.4.6.2. Australia Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.4.6.3. Australia Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
• 6.5. Latin America
  • 6.5.1. Latin America Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.5.1.1. Latin America Self-supervised Learning Market Estimates and Forecasts, by Country, 2018 - 2030 (USD Million)
    • 6.5.1.2. Latin America Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.5.1.3. Latin America Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
  • 6.5.2. Brazil
    • 6.5.2.1. Brazil Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.5.2.2. Brazil Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.5.2.3. Brazil Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
• 6.6. Middle East and Africa
  • 6.6.1. Middle East and Africa Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.6.1.1. Middle East and Africa Self-supervised Learning Market Estimates and Forecasts, by Country, 2018 - 2030 (USD Million)
    • 6.6.1.2. Middle East and Africa Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.6.1.3. Middle East and Africa Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
  • 6.6.2. UAE
    • 6.6.2.1. UAE Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.6.2.2. UAE Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.6.2.3. UAE Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
  • 6.6.3. KSA
    • 6.6.3.1. KSA Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.6.3.2. KSA Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.6.3.3. KSA Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)
  • 6.6.4. South Africa
    • 6.6.4.1. South Africa Self-supervised Learning Market Estimates and Forecasts, 2018 - 2030 (USD Million)
    • 6.6.4.2. South Africa Self-supervised Learning Market Estimates and Forecasts, by End Use, 2018 - 2030 (USD Million)
    • 6.6.4.3. South Africa Self-supervised Learning Market Estimates and Forecasts, by Technology, 2018 - 2030 (USD Million)

Chapter 7. Competitive Landscape

• 7.1. Recent Developments & Impact Analysis, By Key Market Participants
• 7.2. Vendor Landscape
  • 7.2.1. Company categorization
  • 7.2.2. List of Key Distributors and channel Partners
  • 7.2.3. List of Potential Customers/Listing
• 7.3. Competitive Dynamics
  • 7.3.1. Competitive Benchmarking
  • 7.3.2. Strategy Mapping
  • 7.3.3. Heat Map Analysis
• 7.4. Company Profiles/Listing
  • 7.4.1. Alphabet Inc. (Google LLC)
    • 7.4.1.1. Participant's overview
    • 7.4.1.2. Financial performance
    • 7.4.1.3. Product benchmarking
    • 7.4.1.4. Strategic initiatives
  • 7.4.2. Amazon Web Services, Inc.
    • 7.4.2.1. Participant's overview
    • 7.4.2.2. Financial performance
    • 7.4.2.3. Product benchmarking
    • 7.4.2.4. Strategic initiatives
  • 7.4.3. Apple Inc.
    • 7.4.3.1. Participant's overview
    • 7.4.3.2. Financial performance
    • 7.4.3.3. Product benchmarking
    • 7.4.3.4. Strategic initiatives
  • 7.4.4. Baidu, Inc.
    • 7.4.4.1. Participant's overview
    • 7.4.4.2. Financial performance
    • 7.4.4.3. Product benchmarking
    • 7.4.4.4. Strategic initiatives
  • 7.4.5. Iberdrola SA Dataiku
    • 7.4.5.1. Participant's overview
    • 7.4.5.2. Financial performance
    • 7.4.5.3. Product benchmarking
    • 7.4.5.4. Strategic initiatives
  • 7.4.6. Databricks
    • 7.4.6.1. Participant's overview
    • 7.4.6.2. Financial performance
    • 7.4.6.3. Product benchmarking
    • 7.4.6.4. Strategic initiatives
  • 7.4.7. DataRobot, Inc.
    • 7.4.7.1. Participant's overview
    • 7.4.7.2. Financial performance
    • 7.4.7.3. Product benchmarking
    • 7.4.7.4. Strategic initiatives
  • 7.4.8. IBM Corporation
    • 7.4.8.1. Participant's overview
    • 7.4.8.2. Financial performance
    • 7.4.8.3. Product benchmarking
    • 7.4.8.4. Strategic initiatives
  • 7.4.9. Meta
    • 7.4.9.1. Participant's overview
    • 7.4.9.2. Financial performance
    • 7.4.9.3. Product benchmarking
    • 7.4.9.4. Strategic initiatives
  • 7.4.10. Microsoft
    • 7.4.10.1. Participant's overview
    • 7.4.10.2. Financial performance
    • 7.4.10.3. Product benchmarking
    • 7.4.10.4. Strategic initiatives
  • 7.4.11. SAS Institute Inc.
    • 7.4.11.1. Participant's overview
    • 7.4.11.2. Financial performance
    • 7.4.11.3. Product benchmarking
    • 7.4.11.4. Strategic initiatives
  • 7.4.12. Tesla
    • 7.4.12.1. Participant's overview
    • 7.4.12.2. Financial performance
    • 7.4.12.3. Product benchmarking
    • 7.4.12.4. Strategic initiatives
  • 7.4.13. The MathWorks, Inc
    • 7.4.13.1. Participant's overview
    • 7.4.13.2. Financial performance
    • 7.4.13.3. Product benchmarking
    • 7.4.13.4. Strategic initiatives

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