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Powering AI machine learning

AI models typically require large datasets to train and improve their accuracy. With Thordata high-quality proxy IPs, you can route requests through servers located in different regions to scrape. Train your large language model (LLM) and other machine learning on diverse datasets.

Optimal scraping concurrency

Custom or automatic IP rotation

City/ASN-level targeting

HTTP(S) & SOCKS5

Overcoming the challenges of collecting training data for AI

Unrestricted access

Use rotating proxy IPs to seamlessly collect various secure and compliant data without triggering bans.

Avoid Data Bias

Collect diverse data to ensure your AI model remains fair and comprehensive. Train with datasets from various industries and regions.

Scrape Real-Time Web Information

Keep your datasets up to date. With proxy services, scrape the latest information and trends from the web in real-time or regularly.

Regional Applicability Testing

Thordata supports precise city/ASN-level targeting, allowing for testing in different locations to ensure excellent performance among diverse audiences.

Scalability of Data Collection

Unlimited concurrent sessions allow for handling multiple requests simultaneously, enabling large-scale scraping of training data for AI projects.

Load Balancing and Reliability

Use proxies to ensure load balancing, obtain clean and structured training data, and improve the reliability of AI models.

Rotating residential IPs for AI data scraping

Use Thordata proxies to bypass restrictions and effortlessly scrape target data.

Developer-friendly integration documentation

Read our API integration documentation to seamlessly integrate proxies with your scripts, ensuring a smooth, uninterrupted scraping experience during AI model data collection.

View documentation

Advantages of Thordata proxies in AI model development

100% ethically sourced

Thordata sources all proxy products ethically, ensuring accurate and high-quality IP addresses.

Avoid IP or other restrictions

Bypass IP bans and CAPTCHAs, maintaining anonymity and unobstructed large-scale collection of public data.

Unmatched proxy quality

99.9% uptime and high response speed, efficiently collecting large amounts of data without delays or downtime.

Global geographic coverage

60M+ IPs from over 195 countries/regions, unrestricted access to internet content worldwide.

User-friendly self-service dashboard

View all proxy data usage through our dashboard, create and manage sub-accounts.

Real-time customer support

24/7 quick and helpful customer support, contact us whenever you need assistance.

Other common use cases

Explore the use cases of Thordata proxies across various industries. Maximize your business potential with our reliable proxy solutions.

E-Commerce

Real-time scraping and monitoring of competitors' inventory and pricing data to maintain a competitive edge.

Brand Protection

Easily collect valuable SEO data and conduct competitor research using city-level high-quality proxies.

Cybersecurity

Use Thordata proxy services to protect your online privacy and effectively prevent data breaches and cyberattack risks.

Data Generation AI

Seamlessly collect high-quality data from any country to enhance AI model training.

Frequently asked questions

What is AI training data?

AI training data is used to train AI or other machine learning models. These datasets are the foundation of any AI model. AI models learn patterns, make decisions, and generate results by studying this data.

Why use proxies for AI model data collection?

To ensure the anonymity, legality, and efficiency of the data collection process. Proxies can help bypass challenges such as IP bans and CAPTCHAs, collecting data from around the world, which is crucial for training diverse and accurate AI models.

How to collect AI model training data?

Ensuring the diversity, quality, and legality of data during the data collection process is crucial. The process of collecting training data for AI models typically includes the following steps:

1. Clearly define the task objectives and data requirements to ensure that the data can represent the problem domain the model aims to solve.

2. Data can be obtained through public datasets, web scraping, sensor collection, user-generated content, and other means.

What types of data are used to train generative AI models?

Generative AI models are trained using various types of data--including text, images, audio, video, code, and both structured and unstructured data.

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