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Training Data for AI Models: A Technical Guide

Acquiring high-quality, large-scale training data is a critical challenge for AI engineers. This guide provides a comprehensive technical overview of Bright Data’s infrastructure for building and managing data acquisition pipelines, designed to help you make informed decisions and get started quickly.

Technical Quick Reference

FeatureSpecification
Data FormatsJSON, NDJSON, CSV, XLSX, and Parquet. Specify your desired format in the API request.
AuthenticationAll API requests are authenticated using a bearer token. Include your API key in the Authorization header.
Data FreshnessArchive: Historical. Pre-collected: Updated daily, weekly, or monthly. Custom: On-demand, near real-time.
ComplianceGDPR, CCPA, and SOC2 compliant. We adhere to a strict ethical framework for all data collection. See our Trust Center.
Developer ToolsWe provide SDKs for Python and Javascript.
Free TrialSign up and receive a credit to test the platform. Download data samples for any dataset before purchasing.

Data Acquisition Strategies

Your strategy for data acquisition depends on your model’s needs. Choose the method that best fits your use case, from foundational training to specialized, real-time data collection.
  • Web Archive
  • Pre-collected Datasets
  • Custom Collection
  • Video & Media
Best for: Foundational, large-scale model training.The Web Archive provides access to a petabyte-scale repository of historical web data, making it the ideal source for training large language and diffusion models that require a comprehensive understanding of the digital world.

Data Delivery

Once your data is collected, it can be delivered to a variety of destinations to seamlessly integrate with your existing cloud infrastructure. Supported Delivery Options:
  • Amazon S3
  • Google Cloud Storage
  • Microsoft Azure Storage
  • Webhook
  • SFTP/FTP
  • Snowflake
  • API Download
For detailed instructions on setting up your preferred delivery method, please see our Delivery Options documentation.