As the use of AI has gone mainstream, so has interest in building custom models. Today, more and more companies are choosing not just to use public models, but to build their own – whether that’s out of concern for security, because they want more specific and targeted outcomes for their use cases, or to improve performance.
Either way, the result is a rush on training data as the make-or-break element in the AI model arms race. A recent survey by Hitachi Vantara revealed that high-quality data is the number one factor that determines the success of AI projects, and 42% of participants cited data as their biggest concern when conducting these projects.
The question is, where is that data coming from? There are several possibilities, including internal data, synthetic data and open-source datasets, but today’s AI developers know that the best option is the public internet. Web data promises real-time, unbiased, compliant and scalable datasets that check all the boxes.
However, accessing this data generally requires a proxy network, especially when you want to source diverse data that reflects different perspectives, or that involves personalized variants based on location and device signatures. Otherwise, your datasets risk being narrow and unbalanced, which doesn’t produce good outcomes when you’re trying to train or finetune a model.
The Importance of the Right Proxy Infrastructure
Many proxy solutions are available, but not all of them are suitable for collecting data from the web at scale. Public proxies are easily available and usually free, but they’re also usually slow and have poor security. Shared proxies are affordable and less slow than public proxies, but using them increases the risk of getting banned or blocked because of someone else’s activity.
Standard VPNs generally aren’t robust enough for collecting training data. They’re intended for personal use like watching sports from other regions or protecting personal security when using public wifi.
This task requires access to a multitude of proxies that are designed for enterprise use cases. Experts may debate the pros and cons of ISPs, or datacenter proxies, vs residential proxies, but while each has features that make it more effective for specific circumstances, they’re both powerful enough for effective training data collection.
Let’s take a closer look at the advantages of using robust proxy networks when compiling training data.
Content Intake Speed
Training data needs to be fresh so that it reflects current scenarios and concerns. Will your custom AI model work with weather patterns, Wall Street movements, ecommerce product price trends or flight schedules, for example? Yesterday’s information won’t cut it.
Ideally you’ll gather this information as it’s published, so it’s crucial to use a proxy that can perform the task quickly. ISPs are generally viewed as the best proxy when speed is of the essence, because they make fewer “hops” between your server and the data source.
Datacenter proxies “are a great option for quick response times and an affordable solution,” recommends Sophia Ellis from The Knowledge Academy. “They are also ideal for quickly gathering intelligence on an individual or organisation, allowing users to harvest data inexpensively and efficiently.”
Getting Around the Blockers
One of the main reasons why you need a proxy network is so that you can collect data from websites that might try to block web scrapers and/or visitors from particular regions or devices. ISPs are able to unblock sites with easy to moderate blocking, but this is where residential proxies really shine, delivering higher success rates and more efficient data extraction.
Because they use a global network of real devices, requests are distributed across multiple IP addresses to safely and ethically avoid detection. This enables them to bypass even advanced detection mechanisms like rate limiting, browser fingerprinting, honeypot traps, and captchas, which need to be solved quickly to support efficient data collection.
Accuracy and Reliability
It’s essential that the training data you collect is relevant and trustworthy, otherwise you’re going to get skewed outcomes. You need to know that the data is precise and accurate for your preferred audience member’s tech configuration, because it can change depending on who’s accessing the webpage.
Residential proxies use real users’ IP addresses, so you see the screen that they view and acquire more accurate data.
“Since residential data requests are routed through real devices, data retrieval times may be a bit slower. But on a positive note, they almost never get blocked, even when attempting to scrape highly sophisticated open-source sites,” says Amit Elia, Control Panel Product Manager at Bright Data. “Information retrieved is almost always accurate based on the GEO, device, or Operating System (OS) of the user’s choice.”
Maintaining Operational Scalability
When you’re sourcing web data for training AI models, scalability matters. You need a lot of data, even if you’re finetuning a small model, and gathering it requires a lot from your proxy infrastructure. Therefore, you need the ability to handle multiple concurrent requests, to provide sufficient bandwidth for high traffic, and to stay on top of distributed data sourcing.
It’s necessary to rotate IPs in order to get around website blocks, monitor data collection logs for compliance and relevance, to integrate the data intake with your data processing workflows, and optimize it all for efficiency. There’s no way to manage this all manually, but with a robust proxy solution that can automate all these processes, you can scale up and down as your training data needs fluctuate.
Compliance and Security
Whichever type of proxies you use, it’s crucial that they comply with data privacy legislation, copyright laws, and ethical web data sourcing standards.
You need proxies that respect the robots.txt files that tell tools which parts of the website they can’t touch, that only pull bona-fide publicly available information, and that avoid overloading website servers with too many requests.
“A reputable proxy provider maintains a pool of clean, well-maintained IPs. Read reviews and avoid providers with a history of being blacklisted by major websites. Look for providers offering transparent information about their data centers and network infrastructure,” advises Ahona Rudra from PowerDMARC.
Control
When you’re running proxies to collect data for AI model training, you need to be able to control which data they gather and which sources they target. Your proxies should support customization, so that you can direct them to collect only the data that’s relevant for your training needs.
It’s also important to use proxies which can navigate through a range of websites with varying structures and complex architecture. This includes handling Javascript rendering, JSON extraction, and HTML parsing, with powerful extraction features.
Don’t Skimp on Your Proxy Network
There are times when you can cut corners, but this isn’t one of them. Training data is the lifeblood of your AI model, and web proxies are key to keeping it flowing. It’s worth investing in robust proxies that you can trust to bring the accurate, diverse, real-time, and high-quality data you need, without compromising on compliance or slowing you down.
