The B2B platform for the best purchasing descision. Identify and compare relevant B2B manufacturers, suppliers and retailers
Close
Filter
Continents
Select continent
Locations
Result types
Company type
Select company type
Industries
Select industry
Company status
Select company status preset
Number of employees
Min.
Max.
Founding year
Data Labeler
Cherry Hill Township, United States
B
11-50 Employees
2018
Key takeaway
The company specializes in creating high-quality, customized labeled datasets for machine learning initiatives through its integrated data labeling platform, ensuring consistency, efficiency, accuracy, and speed. With experience in over 200 projects, they provide essential data labeling services that enable companies to focus on their core AI and ML operations.
Reference
Service
Services - Data Labeling Services | Data Annotations | AI and ML
INFOLKS
Mannarkkad, India
D
501-1000 Employees
2016
Key takeaway
INFOLKS is a leading data labeling company in India, emphasizing the importance of high-quality data labeling services. With over 7 years of experience and a skilled workforce, they ensure timely and customizable solutions for various data labeling needs, making them a key player in the AI/ML sector.
Reference
Core business
Data Labeling Service Provider For AI/ML | INFOLKS
LabelOps
Mannarkkad, India
D
51-100 Employees
2020
Key takeaway
LabelOps is recognized as the leading data labeling company globally, offering cost-effective data annotation services and extensive experience in handling large datasets. They provide 24/7 customer support and ensure premium quality through a dedicated project team and expert annotators, making them well-equipped to prepare high-quality training data for AI models.
Reference
Service
Image Annotation Services and Data labeling for AI Models | LabelOps
LabelOps process quality data for training computer vision & machine learning models through fully managed image annotation services .
Looking for more accurate results?
Find the right companies for free by entering your custom query!
25M+ companies
250M+ products
Free to use
Label Your Data
Kyiv, Ukraine
B
251-500 Employees
2020
Key takeaway
The company offers high-end data labeling services, emphasizing security and compliance with ISO, PCI DSS, GDPR, and CCPA standards. Their focus on secure data annotation, particularly for LiDAR sensor fusion, ensures that sensitive data is handled with care.
Reference
Core business
Company Label Your Data – Data Annotation for LiDAR Sensor Fusion
Enterprise-class secure data labeling service.
Empowera Technorganics Private Limited
India
D
11-50 Employees
1982
Key takeaway
Empowera is a leading manufacturer of specialty emulsions and adhesives, focusing on delivering unique value across various industries. Their expertise in water-based adhesives and coatings highlights their commitment to enhancing production and process efficiency.
Reference
Product
Labeling
TPL
East Kilbride, United Kingdom
A
11-50 Employees
1969
Key takeaway
TPL is one of the UK's leading wet glue label printers, offering a range of in-house industrial label printing systems. Their advanced printing technology ensures high-quality labels and tags, making them well-equipped for data labeling needs.
Reference
Service
Labelling | TPL Labels
SIBAI VIETNAM
Hà Nội, Vietnam
D
251-500 Employees
2020
Key takeaway
SIBAI is a specialized data labeling company in Vietnam that offers comprehensive data annotation services to enhance business growth. With a dedicated team of over 200 experienced specialists, SIBAI ensures accuracy and efficiency in annotating unstructured data across various formats, making it a valuable partner for businesses looking to train AI algorithms effectively.
Reference
Core business
Data Labeling Company in Vietnam - SIBAI - BPO Services in Viet Nam
We are a dedicated data labeling company in Vietnam that can annotate every unstructured piece of data, on multiple platforms, across all content types.
Labelata
Zurich, Switzerland
A
1-10 Employees
2020
Key takeaway
Labelata specializes in medical data labeling, offering manual segmentation and labeling of anatomical structures in various medical images. Their network of experienced radiologists ensures quality assurance for AI projects in medical research.
Reference
Core business
Medical Data Labelling | Labelata
Labelata enables Medical AI through High Quality Data Labelling
Flipside AI
Quezon City, Philippines
D
51-100 Employees
2010
Key takeaway
Flipside is a leader in data labeling for computer vision, specializing in applications for autonomous vehicles, advanced driver assistance systems, and robotics. Their expertise includes complex 3D, LiDAR, and RADAR sensor fusion, ensuring high-accuracy and ethical AI data annotation.
Reference
Core business
Data Labeling - Flipside AI
fully-managed Humans in the Loop expert + ethical AI data annotation
Alectio
United States
B
11-50 Employees
2019
Key takeaway
Alectio emphasizes the importance of data labeling as a critical step in preparing datasets. Their Smart Labeling Solutions help identify the most informative records, reducing labeling costs and training times, ultimately enhancing model performance.
Reference
Product
Smart Labeling Solutions - Alectio the DataPrepOps company
Data Labeling is arguably one of the most critical steps when preparing a dataset. Labeling doesn’t have to be hard: introducing our Smart Labeling Solutions.
Technologies which have been searched by others and may be interesting for you:
A selection of suitable products and services provided by verified companies according to your search.
Service
Data Annotation
Go to product
Data labeling is the process of annotating and tagging data to make it understandable for machine learning models. This involves assigning labels to various types of data, such as images, text, or audio, to provide context that enables algorithms to learn from the data effectively. For instance, in image recognition tasks, data labeling might include identifying objects within images or categorizing scenes. This crucial step enhances the model's ability to make accurate predictions and classifications, ultimately improving the performance of AI applications.
Data labeling plays a crucial role in enhancing machine learning models by providing the necessary training data. When datasets are accurately labeled, algorithms can learn to recognize patterns, make predictions, and improve their accuracy. Quality labeled data ensures that the model understands the context and features relevant to specific tasks, which is vital for tasks like image recognition, natural language processing, and sentiment analysis. Moreover, consistent labeling helps in reducing bias and enhancing the generalization of the model. When data is labeled correctly, it minimizes the chances of misinterpretation, leading to improved performance during testing and real-world application. The more precise and comprehensive the labeled data is, the better the machine learning model becomes at delivering reliable and actionable outcomes.
Common challenges in data labeling include maintaining high quality and consistency across labeled datasets. Inaccuracies can arise from human error, leading to misclassification and negatively impacting model performance. Additionally, scaling labeling efforts to meet large dataset requirements often proves difficult, as it necessitates a significant workforce and stringent quality control measures. Another challenge involves dealing with ambiguous data. For instance, images or text that can be interpreted in multiple ways pose difficulties in achieving a standardized labeling approach. Furthermore, keeping up with evolving data types and the need for continuous retraining can add complexity to the labeling process. Overall, these challenges require strategic solutions to ensure effective data labeling.
Various tools are utilized for data labeling, each designed to enhance the efficiency and accuracy of the labeling process.
1. Annotation Tools
Software like Labelbox, VGG Image Annotator, and RectLabel are popular choices for image and video annotation. These tools offer user-friendly interfaces that streamline the labeling of visual data.
2. Text Annotation Tools
For text data, tools such as Prodigy and Doccano facilitate the labeling of text documents. These platforms support multiple labeling formats, making it easier to categorize and tag text data effectively.
3. Audio Annotation Tools
In the realm of audio data, tools like Audacity and WaveSurfer allow for precise labeling of audio files. These tools help in annotating speech, sound events, and other audio elements for various applications.
Overall, the choice of tools often depends on the specific type of data being annotated and the requirements of the project.
Data labeling is crucial for AI development as it provides the structured information necessary for machine learning algorithms to understand and interpret data accurately. By assigning labels to various data points, such as images, text, or audio, the models learn to recognize patterns and make predictions based on those inputs. This process enhances the quality of the training data, allowing AI systems to improve performance and reduce errors in real-world applications. Furthermore, the effectiveness of an AI model heavily relies on the quality and quantity of labeled data. Well-labeled datasets help in generating more reliable models, which in turn leads to better decision-making and outcomes in various fields such as healthcare, finance, and autonomous vehicles. Investing in high-quality data labeling can significantly impact the success of AI initiatives.
Some interesting numbers and facts about your company results for Data Labeling
| Country with most fitting companies | United States |
| Amount of fitting manufacturers | 3162 |
| Amount of suitable service providers | 3334 |
| Average amount of employees | 51-100 |
| Oldest suiting company | 1969 |
| Youngest suiting company | 2020 |
20%
40%
60%
80%
Some interesting questions that has been asked about the results you have just received for Data Labeling
What are related technologies to Data Labeling?
Based on our calculations related technologies to Data Labeling are Big Data, E-Health, Retail Tech, Artificial Intelligence & Machine Learning, E-Commerce
Who are Start-Ups in the field of Data Labeling?
Start-Ups who are working in Data Labeling are Labelata
Which industries are mostly working on Data Labeling?
The most represented industries which are working in Data Labeling are IT, Software and Services, Other, Printing, Marketing Services, Consulting
How does ensun find these Data Labeling Companies?
ensun uses an advanced search and ranking system capable of sifting through millions of companies and hundreds of millions of products and services to identify suitable matches. This is achieved by leveraging cutting-edge technologies, including Artificial Intelligence.
