Data Collection for Machine Learning
We provide data collection services using both automated and manual methods, ensuring data accuracy for your ML models
Trusted by the world’s leading tech brands
Data Collection Methods
Rendering synthetic data
Creating data based on specific parameters to model non-existent scenarios and train models without the risk of violating personal data handling rules.
Parsing and web scraping
An automated process for collecting and sorting data with specific parameters and defined attributes. This involves finding the necessary data on websites, forums, portals, online stores, etc., using parsers developed by us.
Crowdsourcing
Collection of data from a wide audience. Allows for obtaining diverse and up-to-date data for training models
Selecting open source datasets
Searching, filtering, and preparing data from open sources and data marketplaces according to technical specifications.
Data Collection Stages
Collection
Selecting appropriate tools and methods for data search according to the technical specifications and business goals.
Cleaning
Structuring and classifying the data by specified attributes to create a high-quality dataset and train the neural network on clean data.
Preparation
Preparing the dataset and metadata in the requested format. Transferring exclusive usage rights and signing all closing documents.
Augmentation
Generating data based on existing datasets using various distortion techniques (shape, color, tilt, etc.), and adding or mixing objects.
Types of Data
Text
We collect text data from various sources, including social networks, blogs, and crowdsourcing platforms. Collecting texts in different languages allows us to cover a wide range of topics and stylistic directions.
Images
Collecting images from open sources and through crowdsourcing platforms. We engage users to create unique images, which helps address a wide range of tasks effectively.
Video
Obtaining unique video recordings needed for specialized projects such as motion analysis, object tracking, or other complex tasks requiring detailed video surveillance.
Lidar
Collecting images from open sources and through crowdsourcing platforms. We engage users to create unique images, which helps address a wide range of tasks effectively.
Dicom
Medical data annotation, including dental and MRI imagesWhy Choose Us
UniData offers unparalleled expertise in AI data solutions, delivering superior data quality and optimized workflows
- 1000 +
- full-time assessors
Expertise
Our team includes top experts in AI data solutionsQuality
We guarantee superior data quality to maximize your AI project's potentialEfficiency
Our optimized workflows accelerate your model training processesProven Results
Our case studies showcase our success in delivering exceptional outcomesCustomization
We tailor our services to fit the specific needs of your AI projectsSupport
We offer ongoing support and consultation to ensure your continued successStages of work
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Application
Submit a request on our website for a free consultation with an expert. Our account manager will provide guidance on services, timelines, and pricing
01 -
Agreement
We will conduct a test pilot project and deliver a golden set, from which we will finalize the technical requirements and project metrics
03 -
Quality control
Data is uploaded for verification iteratively, allowing your team to review and approve the collected or annotated data
05 -
Free pilot
We will conduct a test pilot project and provide a golden set, which will help us finalize the technical requirements and approve project metrics
02 -
Workflow customization
We will select the most suitable tools and assign an experienced manager to coordinate all project details
04 -
Post-payment
Payment is made after you receive the data meeting the agreed quality and quantity
06
Timeline
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24 hours
Application
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24 hours
Consultation
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1 to 3 days
Pilot
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1 to 5 days
Conducting a pilot
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1 day to several years
Carrying out work on the project
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1 to 5 days
Quality control
You pay for the work after you have received the data in the established quality and quantity
