The Global Data Annotation Tools Market size is expected to reach $2. 3 billion by 2027, rising at a market growth of 24. 4% CAGR during the forecast period. A data annotation tool refers to a cloud-based, containerized, or on premise software solution that helps in annotating production-grade training datasets for machine learning.
New York, Aug. 31, 2021 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Data Annotation Tools Market By Type, By Annotation Type, By Industry, By Regional Outlook, Industry Analysis Report and Forecast, 2021 - 2027" - https://www.reportlinker.com/p06131311/?utm_source=GNW
There are a few companies that adopt a do-it-yourself approach and develop their own equipment; however, several data annotation tools are accessible through open source or freeware that are adopted by many organizations. These solutions are provided commercially for purchase and lease. Data annotation tools are mainly developed for their usage in some specific kinds of data, like video, image, audio, spreadsheet, text, or sensor data. In addition, these tools also provide various deployment models that include on premise, SaaS (cloud), container, and Kubernetes.
In the machine learning domain, the process of labelling data to demonstrate the result a company want its machine learning model to forecast is termed as data annotation. Under this process, a dataset is labeled, marked, transcribed, tagged, or processed with the required features by its machine learning system to learn to identify. After the implementation of the model, the system identifies those features itself and responds to them. Annotated data uncovers features, which would train a company’s algorithms to recognize those same features in the dataset that has not been annotated. Data annotation is majorly utilized in supervised learning & hybrid, machine learning models, or semi-supervised, which include supervised learning.
The outbreak of the global covid-19 pandemic has affected every domain of society be it individuals or businesses. The several impositions rolled out by the governments across the globe to curb the spread of the coronavirus have disturbed the global supply of goods & services. The nationwide lockdown and temporary shutdown of the manufacturing units in the pandemic have resulted in the decreased labor on the supply side and a reduction in the demand for products & services on the other side.
All such restrictions have negatively affected the data annotation tools market. In addition, owing to the declining GDP, the market would witness a negative impact on the growth of the data annotation tools market. Though, it is estimated that the economy will get back on track and hence the data annotation tools market will also register a significant demand.
Based on Type, the market is segmented into Text, Image/Video and Audio. The text segment acquired the highest revenue share in the data annotation market in 2020. The image/video annotation segment is projected to witness a significant growth rate during the forecast period. Some of the key applications of image data annotation are in the medical sector in the domain of medical imaging. The text segment is anticipated to exhibit a substantial CAGR during the forecast period due to the increasing applications of data annotation tools in clinical research and e-commerce applications. The audio segment is projected to garner the fastest growth rate during the forecast period.
Annotation Type Outlook
Based on Annotation Type, the market is segmented into Manual, Semi-supervised and Automatic. The manual segment procured the largest revenue share in the data annotation market in 2020. Manual data annotation refers to the process of annotating or labeling any generated dataset by humans. This approach is gaining traction owing to its advantages like high level of integrity, precision, minimal data annotation efforts, and more chance of generating intriguing insights related to the data in comparison to automatic annotation that could be combined into an algorithm in the future. Though, manual annotation is costly and time-consuming, thus labeled data collected via crowd sourcing activities are utilized for several applications.
Based on Industry, the market is segmented into IT, Automotive, Retail, Healthcare, Financial Services, Government and Others. The automotive market segment is estimated to witness the fastest growth rate during the forecast period. It is due to the wide acceptance of data annotation tools in self-driving vehicles. The increasing investment in R&D activities in order to improve the image annotation for boosting the developments in the area of self-driving vehicles would surge the growth of the market.
Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa. North America emerged as a dominant region with the largest revenue share in the data annotation tools market in 2020. This growth is attributed to the quick product & geographical expansion strategy adopted by the key market players to achieve a competitive edge in the market.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Google, Inc., Amazon.com, Inc., Uber Technologies, Inc. (Mighty AI, Inc.), Appen Limited, Scale AI, Inc., Labelbox, Inc., Annotate Software, Cogito Tech LLC, Deep Systems, and Playment, Inc.
Strategies deployed in Data Annotation Tools Market
May-2021: Labelbox teamed up with Databricks, an enterprise software company. Together, the companies announced the features for teams to develop unstructured data for AI and analytics in Databricks. By integrating Databricks and Labelbox, users get an end-to-end surrounding for unstructured data workflows, a query engine developed around Delta Lake, quick annotation tools, and a strong Machine Learning compute environment.
Feb-2021: Appen Limited introduced the new & improved capabilities. These capabilities aimed to make sure that autonomous vehicle manufacturers get access to high-quality training data and can attain the most value from their training data investment.
Feb-2021: Google Cloud formed a partnership with NextBillion AI, an industry-leading startup in mapping platforms. The partnership aims to enhance time-to-market for hyperlocal AI solutions by operating datasets & algorithms on Cloud Storage & Cloud SQL to reduce the operational overheads with Google Kubernetes Engine.
Dec-2020: Labelbox came into partnership with TensorFlow, a free and open-source software library for machine learning. Under this collaboration, the TensorFlow team would enable more teams to boost their ML projects. It consists of regularly sharing insights & product feedback and also working together to carry out improvements and advancements, which especially fulfil enterprise ML requirements.
Aug-2020: Appen came into partnership with the World Economic Forum. Under this partnership, Appen would collaborate with key leaders to introduce the new standards in shaping the Future of Technology Governance Artificial Intelligence and Machine Learning platform that enables global footprint and guidepost for better training data collection and development over nations and industries.
May-2020: Labelbox entered into a partnership with Carahsoft Technology. In this partnership, Carahsoft would serve as Labelbox’s Master Government Aggregator that makes the company’s platform accessible via Carahsoft’s NASA Solutions for Enterprise-Wide Procurement (SEWP) contracts & reseller partners.
Dec-2019: Appen Limited unveiled the Appen Secure Workspace Solution. This solution offers the correct combination of technology, people, and procedure to make sure that projects including sensitive data can be annotated by a global crowd safely.
Sep-2019: Amazon launched the Topical Chat Dataset, a text-based collection of more than 235,000 utterances. This launch enables innovative research in knowledge-grounded neural response-generation systems by facing the hard challenges, which are not supported by other publicly accessible datasets.
Apr-2019: Appen Limited acquired Figure Eight, a human-in-the-loop machine learning and artificial intelligence company. This acquisition aimed to combine Appen’s skilled & diverse global workforce of more than 1 million global contractors with Figure Eight’s industry-leading data annotation platform making strong training data capabilities developed to improve AI initiatives for a broad range of use cases.
Dec-2018: Scale AI formed a partnership with Ouster, a leading manufacturer of high-resolution lidar sensors, along with Playment. This partnership focused on swiftly boosting the incorporation of deep learning models in self-driving vehicles & other real-time robotics by resolving their biggest challenge accurate and multi-layer labelled training data.
Jul-2018: Google joined hands with Figure Eight, a platform that helps developers train, test and fine-tunes their machine learning models. This collaboration importantly turns Figure Eight into the de facto standard for making & annotating machine learning data for Google Cloud’s AutoML service.
Nov-2017: Appen Limited entered into an agreement to acquire Leapforce, a leading provider of search relevance services with a highly automated and proprietary end-to-end technology platform. The combination of Leapforce’s highly automated technology platform and Appen’s 20+ year track record of providing high-quality, human-annotated data and the integrated crowd of almost 1.2 million workforces would enable scalability, automation, and efficiency enhancements to offer better quality, responsiveness and value to clients across the globe.
Scope of the Study
Market Segments covered in the Report:
By Annotation Type
• Financial Services
• North America
o Rest of North America
o Rest of Europe
• Asia Pacific
o South Korea
o Rest of Asia Pacific
o Saudi Arabia
o South Africa
o Rest of LAMEA
• Google, Inc.
• Amazon.com, Inc.
• Uber Technologies, Inc. (Mighty AI, Inc.)
• Appen Limited
• Scale AI, Inc.
• Labelbox, Inc.
• Annotate Software
• Cogito Tech LLC
• Deep Systems
• Playment, Inc.
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