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Global Self-supervised Learning Market to reach 70.20 billion by the end of 2029

Global Self-supervised Learning Market Size study & Forecast, by Technology (Natural Language Processing (NLP), Computer Vision, Speech Processing), by End-User (Healthcare, BFSI, Automotive & Transportation, Software Development (IT), Advertising & Media, Others) and Regional Analysis, 2022-2029

Product Code: ENGE-18634045
Publish Date: 8-10-2022
Page: 200

Global Self-supervised Learning Market is valued at approximately USD 7.0 billion in 2021 and is anticipated to grow with a healthy growth rate of more than 33.4% over the forecast period 2022-2029. Recently, the field of artificial intelligence (AI) has undergone tremendous progress in emerging AI systems that can learn from large amounts of prudently labeled data. In this context, the paradigm of supervised learning has an evident track record for training specialist models that perform exceptionally well on the task that they are trained to do. Supervised learning has a range of applications, including text categorization, face detection, and colorization. In addition, it has applications in several industries such as healthcare, automotive & transportation, BFSI, advertising & media, software development, and others. Factors, such as the increasing applications of technologies such as face detection and voice recognition, along with surge in demand to streamline workflow across industries are driving the growth of the global self-supervised learning market.

As per the Department of Ministry of Corporate Affairs (MCA) the adoption of virtual assistants with speech recognition capabilities were set to rise from 60.5 million people across the U.S. in 2017 to 62.4 million in 2018. Also, around 66.6 million Americans are projected to adopt speech or voice recognition technology by the year 2019. Thus, the rise in usage of virtual assistant technology will spur the demand for self-supervised learning market. In addition, increasing R&D activities in technology companies, as well as growing awareness of the benefits of supervised learning among end-users are creating various lucrative prospects for the market over the forthcoming years. However, lack of skilled workforce in the field of supervised learning is one of the major factors that is restraining the market growth throughout the forecast period of 2022-2029.

The key regions considered for the Global Self-supervised Learning Market study include Asia Pacific, North America, Europe, Latin America, and Rest of the World. North America dominated the space in terms of revenue, owing to the rising emphasis on the emergence of novel technologies, increasing investment in R&D activities, and presence of leading market players. Whereas, Asia Pacific is expected to grow significantly during the forecast period, owing to factors such as increasing adoption of self-supervised learning applications, as well as rising number of government initiatives in AI solutions.

Major market players included in this report are:
IBM
Alphabet Inc. (Google LLC)
Microsoft
Amazon Web Services, Inc.
SAS Institute Inc.
Dataiku
Apple Inc.
Tesla
Databricks
DataRobot, Inc.
Recent Developments in the Market:
 In February 2022, IBM broadcasted that the company acquire Neudesic, under its hybrid cloud and AI strategy. Neudesic adds expertise in data engineering, data analytics, and deep Azure cloud. The aim of this acquisition is to strengthen its cloud service skills and capabilities to fulfill the client’s demands.
 In January 2022, Meta AI declared the launch of data2vec-a self-supervised learning algorithm that works for text, vision, and speech. This algorithm is designed to outperform previous algorithms for speech and computer vision.
Global Self-supervised Learning Market Report Scope:
Historical Data 2019-2020-2021
Base Year for Estimation 2021
Forecast period 2022-2029
Report Coverage Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
Segments Covered Technology, End-User, Region
Regional Scope North America; Europe; Asia Pacific; Latin America; Rest of the World
Customization Scope Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:
By End-User:
Healthcare
BFSI
Automotive & Transportation
Software Development (IT)
Advertising & Media
Others

By Technology:
Natural Language Processing (NLP)
Computer Vision
Speech Processing

By Region:
North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE
Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
Rest of the World

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2029 (USD Billion)
1.2.1. Self-supervised Learning Market, by Region, 2019-2029 (USD Billion)
1.2.2. Self-supervised Learning Market, by Technology, 2019-2029 (USD Billion)
1.2.3. Self-supervised Learning Market, by End-User, 2019-2029 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Self-supervised Learning Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Self-supervised Learning Market Dynamics
3.1. Self-supervised Learning Market Impact Analysis (2019-2029)
3.1.1. Market Drivers
3.1.1.1. Increasing applications of technologies such as face detection and voice recognition
3.1.1.2. Surge in demand to streamline workflow across industries
3.1.2. Market Challenges
3.1.2.1. Lack of skilled workforce in the field of supervised learning
3.1.3. Market Opportunities
3.1.3.1. Increasing R&D activities in technology companies
3.1.3.2. Growing awareness of the benefits of supervised learning among end-users
Chapter 4. Global Self-supervised Learning Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Futuristic Approach to Porter’s 5 Force Model (2019-2029)
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.4. Investment Adoption Model
4.5. Analyst Recommendation & Conclusion
4.6. Top investment opportunity
4.7. Top winning strategies
Chapter 5. Risk Assessment: COVID-19 Impact
5.1. Assessment of the overall impact of COVID-19 on the industry
5.2. Pre COVID-19 and post COVID-19 Market scenario
Chapter 6. Global Self-supervised Learning Market, by End-User
6.1. Market Snapshot
6.2. Global Self-supervised Learning Market by End-User, Performance – Potential Analysis
6.3. Global Self-supervised Learning Market Estimates & Forecasts by End-User, 2019-2029 (USD Billion)
6.4. Self-supervised Learning Market, Sub Segment Analysis
6.4.1. Healthcare
6.4.2. BFSI
6.4.3. Automotive & Transportation
6.4.4. Software Development (IT)
6.4.5. Advertising & Media
6.4.6. Others
Chapter 7. Global Self-supervised Learning Market, by Technology
7.1. Market Snapshot
7.2. Global Self-supervised Learning Market by Technology, Performance – Potential Analysis
7.3. Global Self-supervised Learning Market Estimates & Forecasts by Technology, 2019-2029 (USD Billion)
7.4. Self-supervised Learning Market, Sub Segment Analysis
7.4.1. Natural Language Processing (NLP)
7.4.2. Computer Vision
7.4.3. Speech Processing
Chapter 8. Global Self-supervised Learning Market, Regional Analysis
8.1. Self-supervised Learning Market, Regional Market Snapshot
8.2. North America Self-supervised Learning Market
8.2.1. U.S. Self-supervised Learning Market
8.2.1.1. Technology breakdown estimates & forecasts, 2019-2029
8.2.1.2. End-User breakdown estimates & forecasts, 2019-2029
8.2.2. Canada Self-supervised Learning Market
8.3. Europe Self-supervised Learning Market Snapshot
8.3.1. U.K. Self-supervised Learning Market
8.3.2. Germany Self-supervised Learning Market
8.3.3. France Self-supervised Learning Market
8.3.4. Spain Self-supervised Learning Market
8.3.5. Italy Self-supervised Learning Market
8.3.6. Rest of Europe Self-supervised Learning Market
8.4. Asia-Pacific Self-supervised Learning Market Snapshot
8.4.1. China Self-supervised Learning Market
8.4.2. India Self-supervised Learning Market
8.4.3. Japan Self-supervised Learning Market
8.4.4. Australia Self-supervised Learning Market
8.4.5. South Korea Self-supervised Learning Market
8.4.6. Rest of Asia Pacific Self-supervised Learning Market
8.5. Latin America Self-supervised Learning Market Snapshot
8.5.1. Brazil Self-supervised Learning Market
8.5.2. Mexico Self-supervised Learning Market
8.6. Rest of The World Self-supervised Learning Market

Chapter 9. Competitive Intelligence
9.1. Top Market Strategies
9.2. Company Profiles
9.2.1. IBM
9.2.1.1. Key Information
9.2.1.2. Overview
9.2.1.3. Financial (Subject to Data Availability)
9.2.1.4. Product Summary
9.2.1.5. Recent Developments
9.2.2. Alphabet Inc. (Google LLC)
9.2.3. Microsoft
9.2.4. Amazon Web Services, Inc.
9.2.5. SAS Institute Inc.
9.2.6. Dataiku
9.2.7. Apple Inc.
9.2.8. Tesla
9.2.9. Databricks
9.2.10. DataRobot, Inc.
Chapter 10. Research Process
10.1. Research Process
10.1.1. Data Mining
10.1.2. Analysis
10.1.3. Market Estimation
10.1.4. Validation
10.1.5. Publishing
10.2. Research Attributes
10.3. Research Assumption

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Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
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