The Latin America, Middle East and Africa Data Labeling Solution And Services Market should witness market growth of 23.7% CAGR during the forecast period (2023-2030).
Several use cases exist for generative AI in data labeling, where it can lessen the amount of manual labor needed to annotate massive datasets. For instance, generative models like GANs can produce bounding boxes or masks around certain objects in an image. It makes image segmentation jobs more effective by lowering the manual labor to annotate huge datasets. For instance, a U.S.-based firm called OpenAI debuted DALL-E in January 2021, a generative AI model that can produce visuals from verbal descriptions. This technique can be applied to data labeling by creating graphics for certain datasets, minimizing the amount of manual labor necessary for labeling.
Additionally, machine learning systems are frequently used to categorize data objects like tweets or news articles. This, in turn, necessitates a particular annotated training dataset, assisting in automatically creating algorithms that categorize new data objects. However, creating such a dataset by hand is a challenging task requiring much effort from the coders. Clinical data collection, which consists primarily of unstructured text documents, has grown in importance as health record systems (HER)use has grown. Statistical NLP (natural language processing) standards have been created to reveal data hidden in clinical literature.
The UAE government predicts the Middle East ICT market to expand from USD 84.23 billion in 2020 to USD 95.05 billion in 2025 at a compound annual growth rate (CAGR) of 2.4%. The Middle East's IT industry is continuously expanding as countries' budgets in 2019 amounted to USD 207 billion. As the Middle East region's fastest-growing technology industry, software spending grew by 11% yearly to a record-high of USD 6.2 billion in 2018. According to predictions, the adoption of AI in computer vision in the MEA will rise from USD 310.71 million in 2019 to USD 5,210.41 million in 2027, expanding at a CAGR of 43.2% over that time. The growth of the market in this region will accelerate as spending on AI technologies rises.
The Brazil market dominated the LAMEA Data Labeling Solution And Services Market by Country in 2022, and would continue to be a dominant market till 2030; thereby, achieving a market value of $1.0 billion by 2030. The Argentina market is experiencing a CAGR of 24.3% during (2023-2030). Additionally, The UAE market would register a CAGR of 23.3% during (2023-2030).
Based on Type, the market is segmented into Image/Video, Text, and Audio. Based on Labeling Type, the market is segmented into Manual, Semi-Supervised, and Automatic. Based on Sourcing Type, the market is segmented into Outsourced, and In-House. Based on Vertical, the market is segmented into IT, Automotive, Financial Services, Government, Healthcare, Retail, and Others. Based on countries, the market is segmented into Brazil, Argentina, UAE, Saudi Arabia, South Africa, Nigeria, and Rest of LAMEA.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Google LLC (Alphabet Inc.), Appen Limited, TELUS International (Playment, Inc.), Yandex N.V., Uber Technologies, Inc. (Mighty AI, Inc.), Zight, Alegion, Inc., Scale AI, Inc., Labelbox, Inc., Cogito Tech LLC
Several use cases exist for generative AI in data labeling, where it can lessen the amount of manual labor needed to annotate massive datasets. For instance, generative models like GANs can produce bounding boxes or masks around certain objects in an image. It makes image segmentation jobs more effective by lowering the manual labor to annotate huge datasets. For instance, a U.S.-based firm called OpenAI debuted DALL-E in January 2021, a generative AI model that can produce visuals from verbal descriptions. This technique can be applied to data labeling by creating graphics for certain datasets, minimizing the amount of manual labor necessary for labeling.
Additionally, machine learning systems are frequently used to categorize data objects like tweets or news articles. This, in turn, necessitates a particular annotated training dataset, assisting in automatically creating algorithms that categorize new data objects. However, creating such a dataset by hand is a challenging task requiring much effort from the coders. Clinical data collection, which consists primarily of unstructured text documents, has grown in importance as health record systems (HER)use has grown. Statistical NLP (natural language processing) standards have been created to reveal data hidden in clinical literature.
The UAE government predicts the Middle East ICT market to expand from USD 84.23 billion in 2020 to USD 95.05 billion in 2025 at a compound annual growth rate (CAGR) of 2.4%. The Middle East's IT industry is continuously expanding as countries' budgets in 2019 amounted to USD 207 billion. As the Middle East region's fastest-growing technology industry, software spending grew by 11% yearly to a record-high of USD 6.2 billion in 2018. According to predictions, the adoption of AI in computer vision in the MEA will rise from USD 310.71 million in 2019 to USD 5,210.41 million in 2027, expanding at a CAGR of 43.2% over that time. The growth of the market in this region will accelerate as spending on AI technologies rises.
The Brazil market dominated the LAMEA Data Labeling Solution And Services Market by Country in 2022, and would continue to be a dominant market till 2030; thereby, achieving a market value of $1.0 billion by 2030. The Argentina market is experiencing a CAGR of 24.3% during (2023-2030). Additionally, The UAE market would register a CAGR of 23.3% during (2023-2030).
Based on Type, the market is segmented into Image/Video, Text, and Audio. Based on Labeling Type, the market is segmented into Manual, Semi-Supervised, and Automatic. Based on Sourcing Type, the market is segmented into Outsourced, and In-House. Based on Vertical, the market is segmented into IT, Automotive, Financial Services, Government, Healthcare, Retail, and Others. Based on countries, the market is segmented into Brazil, Argentina, UAE, Saudi Arabia, South Africa, Nigeria, and Rest of LAMEA.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Google LLC (Alphabet Inc.), Appen Limited, TELUS International (Playment, Inc.), Yandex N.V., Uber Technologies, Inc. (Mighty AI, Inc.), Zight, Alegion, Inc., Scale AI, Inc., Labelbox, Inc., Cogito Tech LLC
Scope of the Study
By Type
- Image/Video
- Text
- Audio
By Labeling Type
- Manual
- Semi-Supervised
- Automatic
By Sourcing Type
- Outsourced
- In-House
By Vertical
- IT
- Automotive
- Financial Services
- Government
- Healthcare
- Retail
- Others
By Country
- Brazil
- Argentina
- UAE
- Saudi Arabia
- South Africa
- Nigeria
- Rest of LAMEA
Key Market Players
List of Companies Profiled in the Report:
- Google LLC (Alphabet Inc.)
- Appen Limited
- TELUS International (Playment, Inc.)
- Yandex N.V.
- Uber Technologies, Inc. (Mighty AI, Inc.)
- Zight
- Alegion, Inc.
- Scale AI, Inc.
- Labelbox, Inc.
- Cogito Tech LLC
Unique Offerings
- Exhaustive coverage
- The highest number of Market tables and figures
- Subscription-based model available
- Guaranteed best price
- Assured post sales research support with 10% customization free
Table of Contents
Chapter 1. Market Scope & Methodology
Chapter 2. Market At a Glance
Chapter 3. Market Overview
Chapter 4. Competition Analysis - Global
Chapter 5. LAMEA Data Labeling Solution and Services Market by Type
Chapter 6. LAMEA Data Labeling Solution and Services Market by Labeling Type
Chapter 7. LAMEA Data Labeling Solution and Services Market by Sourcing Type
Chapter 8. LAMEA Data Labeling Solution and Services Market by Vertical
Chapter 9. LAMEA Data Labeling Solution and Services Market by Country
Chapter 10. Company Profiles
Companies Mentioned
- Google LLC (Alphabet Inc.)
- Appen Limited
- TELUS International (Playment, Inc.)
- Yandex N.V.
- Uber Technologies, Inc. (Mighty AI, Inc.)
- Zight
- Alegion, Inc.
- Scale AI, Inc.
- Labelbox, Inc.
- Cogito Tech LLC
Methodology
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