Supervised learning relies heavily on large volumes of high-quality labeled data, which is very costly and time-consuming. This is a huge limitation in the domains such as medical imaging, where only expert medical professionals can manually annotate the data. Moreover, Developers who want to create an image classification algorithm, therefore, create supervised learning-capable systems to collect comprehensive data to get a representative sample. Apart from feeding the computer image datasets, developers need to classify the images before they can be used for training. The process is arduous and time-consuming compared with how humans approach learning. Human learning process is multifaceted. It involves both supervised and unsupervised learning processes.
The self-supervised learning market is segmented on the basis of technology, industry vertical, and region. By technology, the market is segmented into natural language processing, computer vision, and speech processing. The natural language processing segment is divided into rule-based NLP, statistical NLP, and hybrid NLP. The computer vision segmented is divided into quality assurance and inspection, positioning & guidance, measurement, identification, and predictive maintenance.
By industry vertical, the market is categorized into BFSI, healthcare, media & entertainment, IT, manufacturing, and others. The BFSI segment is further categorized into banking, financial services, and insurance. The segment is further categorized into life insurance and non-life insurance. Region wise, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
The key players operating in the market include Amazon Web Service (AWS), Alison, Alphabet, Apple, Inc., Baidu, Inc., Brain4ce Education Solutions Pvt. Ltd., DataCamp, Inc., Dataiku, Databricks, Datarobot, Inc., EDX LLC., International Business Machine (IBM), Microsoft Corporation, Meta, SAS Institute, The MathWorks, Inc., and Tesla. These key players have adopted numerous key development strategies such as partnership and new product launches, which help to strengthen their grip in the self-supervised identity market.
Key Benefits For Stakeholders
- This report provides a quantitative analysis of the market segments, current trends, estimations, and dynamics of the self supervised learning market analysis from 2021 to 2031 to identify the prevailing self supervised learning market opportunities.
- The market research is offered along with information related to key drivers, restraints, and opportunities.
- Porter's five forces analysis highlights the potency of buyers and suppliers to enable stakeholders make profit-oriented business decisions and strengthen their supplier-buyer network.
- In-depth analysis of the self supervised learning market segmentation assists to determine the prevailing market opportunities.
- Major countries in each region are mapped according to their revenue contribution to the global market.
- Market player positioning facilitates benchmarking and provides a clear understanding of the present position of the market players.
- The report includes the analysis of the regional as well as global self supervised learning market trends, key players, market segments, application areas, and market growth strategies.
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Key Market Segments
By Technology
- Natural Language Processing
- NLP Type
- Rule Based NLP
- Statistical NLP
- Hybrid NLP
- Computer Vision
- Computer Vision Application
- Quality Assurance and Inspection
- Positioning and Guidance
- Measurement
- Identification
- Predictive Maintenance
- Speech Processing
By Industry Vertical
- BFSI
- BFSI Type
- Banking
- Financial Services
- Insurance
- Healthcare
- Media and Entertainment
- IT
- Manufacturing
- Others
By Region
- North America
- U.S.
- Canada
- Europe
- UK
- Germany
- France
- Italy
- Spain
- Netherlands
- Rest Of Europe
- Asia-Pacific
- China
- Japan
- India
- Australia
- South Korea
- Rest Of Asia-Pacific
- LAMEA
- Latin America
- Middle East
- Africa
- Key Market Players
- Dataiku
- SAS Institute
- Meta
- Databricks
- Apple, Inc.
- Tesla
- DataCamp, Inc.
- edX LLC.
- IBM Corporation
- Alphabet Inc. (Google LLC)
- Microsoft Corporation
- The MathWorks, Inc.
- DataRobot, Inc.
- Alison
- Baidu, Inc.
- Brain4ce Education Solutions Pvt. Ltd.
- Amazon Web Series
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Table of Contents
Executive Summary
According to the report, the self supervised learning market size was valued at $7.5 billion in 2021, and is estimated to reach $126.8 billion by 2031, growing at a CAGR of 33.1% from 2022 to 2031.Self-supervised learning (SSL) is an evolving machine learning technique poised to solve challenges posed by the over-dependence of labeled data. For many years, building intelligent systems using machine learning methods has been largely dependent on good quality labeled data. Consequently, cost of high-quality annotated data is a major bottleneck in the overall training process. As per IBM’s global AI adoption index 2022 report, 34% of respondents believed that a lack of AI skills restrain adoption of AI for businesses. Self-supervised learning is at a growing stage that requires skilled workforce for development. Hence, lack of skilled workforce is expected to restrain the self supervised learning market growth.
Furthermore, increase automation in banking processes and increase use of internet and connected devices is boosting the growth of the global self supervised learning market. In addition, rise in demand for predictive analytics is positively impacts self supervised learning market growth. However, lack of skilled workforce for machine learning is hampering the self-supervised learning market growth. On the contrary, rapid changes in business model technology is expected to offer remunerative opportunities for expansion during the self supervised learning market forecast.
On the basis of technology, the natural language processing segment dominated the self supervised learning market share in 2021, and is expected to maintain its dominance in the upcoming years. NLP has been a widely adopted technology of self-supervised learning across the world as it performs a very large-scale analysis. NLP offers an accurate and objective analysis that reduces most probable human errors Most of the modern-day applications and devices for instance mobile phones and laptops are utilizing NLP technology for enhanced consumer experience. Moreover, NLP reduces the cost by streamlining the processes thus, many enterprises are attracted towards these platforms. Furthermore, NLP empowers humans to not only just save time but efforts to focus on other more important tasks. These factors propel the market growth further.
Depending on North America is anticipated to account for the largest share of the self-supervised learning market during the forecast period, owing to presence of a substantial industrial base in the U.S., government initiatives to promote innovation, and large purchasing power. Growth is primarily concentrated in the U.S. Companies that use big data software frequently use print management systems to cut costs, improve industry vertical, and boost worker productivity. However, Asia-Pacific is expected to witness significant growth during the forecast period, owing to growing economies such as India and China and cloud native countries like Japan.
The current estimation of 2031 is projected to be higher than pre-COVID-19 estimates. The COVID-19 outbreak has high impact on the growth of self-supervised learning market, as increasing number of smartphone users, growing adoption of connected devices, and surging e-commerce sector provide lucrative opportunities for the growth of the self-supervised learning market. COVID has caused crises in social, economic, and energy areas and medical life worldwide throughout 2020. This crisis had many direct and indirect effects on all areas of society. In the meantime, the digital and artificial intelligence industry can be used as a professional assistant to manage and control the outbreak of the virus. In post-pandemic circumstances, enterprises strived to minimize operational and running costs around all the business functions to recover the losses incurred in covid times. The market for self-supervised learning observed unconstructive expansion, during the initial half of 2020. Owing to the limitations due to the global lockdown, media houses, a variety of offices, and the manufacturing divisions have observed a provisional shutting down. The demand for self-supervised learning is anticipated to gain steady traction over the coming years owing to the need for scalable and customized software. Due to the COVID-19 pandemic, increasing health awareness among individuals has directed various doctors and health workers to deliver their services over applications.
KEY FINDINGS OF THE STUDY
By type, the natural language processing segment dominated the self-supervised learning industry in 2021. However, the computer vision segment is expected to exhibit significant growth during the forecast period.On the basis of industry vertical, the BFSI segment dominated the self-supervised learning market in 2021. However, the media and entertainment segment is expected to witness the highest growth rate during the forecast period.
Region-wise, the self supervised learning market analysis was dominated by North America in 2021. However, Asia-Pacific is expected to witness significant growth in the coming years.
The key players operating in the Self Supervised Learning Industry include Amazon Web Service (AWS), Alison, Alphabet, Apple, Inc., Baidu, Inc., Brain4ce Education Solutions Pvt. Ltd., DataCamp, Inc., Dataiku, Databricks, Datarobot, Inc., EDX LLC., International Business Machine (IBM), Microsoft Corporation, Meta, SAS Institute, the MathWorks, Inc., and Tesla. These major players have adopted various key development strategies such as business expansion, new product launches, and partnerships, which propel growth of the self supervised learning industry globally.
Companies Mentioned
- Dataiku
- SAS Institute
- Meta
- Databricks
- Apple, Inc.
- Tesla
- DataCamp, Inc.
- edX LLC.
- IBM Corporation
- Alphabet Inc. (Google LLC)
- Microsoft Corporation
- The MathWorks, Inc.
- DataRobot, Inc.
- Alison
- Baidu, Inc.
- Brain4ce Education Solutions Pvt. Ltd.
- Amazon Web Series
Methodology
The analyst offers exhaustive research and analysis based on a wide variety of factual inputs, which largely include interviews with industry participants, reliable statistics, and regional intelligence. The in-house industry experts play an instrumental role in designing analytic tools and models, tailored to the requirements of a particular industry segment. The primary research efforts include reaching out participants through mail, tele-conversations, referrals, professional networks, and face-to-face interactions.
They are also in professional corporate relations with various companies that allow them greater flexibility for reaching out to industry participants and commentators for interviews and discussions.
They also refer to a broad array of industry sources for their secondary research, which typically include; however, not limited to:
- Company SEC filings, annual reports, company websites, broker & financial reports, and investor presentations for competitive scenario and shape of the industry
- Scientific and technical writings for product information and related preemptions
- Regional government and statistical databases for macro analysis
- Authentic news articles and other related releases for market evaluation
- Internal and external proprietary databases, key market indicators, and relevant press releases for market estimates and forecast
Furthermore, the accuracy of the data will be analyzed and validated by conducting additional primaries with various industry experts and KOLs. They also provide robust post-sales support to clients.
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