AI Data Acquisition Agent: Specialized Data Agent for Large Model Training

When Big Models Meet Data Hunger

Recently, AI engineer Zhang was worried about his half-trained dialog model, which suddenly started to talk nonsense. Upon closer inspection, he realized that the crawled news data had been mixed with a large number of phishing websites - it was like feeding the robot spoiled takeout, not to mention eating a bad stomach, and the whole training progress was delayed.

This situation is too common in the industry. Ordinary crawlers directly connected to the collection is like running naked on the Internet, not only is it easy to be blocked by the target site IP, but also may collect distorted data. At this time, we need to give the data collection on the "cloak", that is, we have to say the proxy IP service.

Three life-saving tricks for proxy IPs

Let's start with a real case: an AI company used a single IP to capture 30,000 times per hour, and as a result, the entire IP segment was blacked out the next day. After switching to a dynamic proxy IP pool, the collection efficiency directly doubled 20 times. There are three key doorways here:

 Bug Demonstration - Naked Capture
import requests
response = requests.get("https://news.example.com")

 The Right Way - Proxy IP Rotation
from rotating_proxy import ProxyPool
proxy = ProxyPool.get_proxy() This is the recommended API for ipipgo.
session = requests.Session()
session.proxies = {"http": proxy, "https": proxy}

Here's the point:A good proxy service has to do three things - enough number of IPs, fast enough switching, and stable enough channels. Take ipipgo for example, their residential proxy pool covers 200+ countries and can switch to a new vest for each request, which is especially suitable for AI projects that require high-frequency acquisition.

The real-world technique of the four-two-thousand pound rule

Many newbies are prone to stepping on the pit is to think that hanging on the agent is all right. In fact, there are a few tips here:

For example, if you do cross-border e-commerce price monitoring, using ipipgo's U.S. residential IP to get the real local price can improve the accuracy of the data collected by more than 60% than using the IP of the server room. Their IP also supports segmentation by city, which is particularly useful for training geographically specific AI models.

question-and-answer session

Q: What should I do if my IP is always blocked when collecting?
A: This means that your IP quality is not good or there is a problem with the switching strategy. It is recommended to try ipipgo's dynamic residential proxy, they have a maximum survival time of no more than 5 minutes per IP and are naturally anti-blocking.

Q: How do I manage thousands of IPs at the same time?
A: Directly with the ready-made proxy management platform is more trouble-free. Like ipipgo provides a browser plug-in can automatically rotate IP, but also with a failure retry mechanism, than self-built proxy pool to save a lot of trouble.

Q: How to judge the quality of proxy IP?
A: Focus on response speed and success rate. Here is a tip: use ipipgo's test interface to run for 24 hours, their statistics panel can directly see the survival of each IP.

The Secret Weapon of Data Collection Teams

Finally, I would like to talk about a way to play that only people in the industry know: combining proxy IP and distributed collection. For example, with 10 servers + ipipgo's 100,000 IP resources, it can realize the real "1,000-sided acquisition". An AI company used this program to save enough corpus data in three months that would have taken two years to acquire.

Here we should pay attention to, do not try to cheap buy poor quality agent. Previously, a team of cheap with wild IP, the result of the collection of data 30% are duplicate content, directly leading to the training of the model suffers from "data malnutrition". Professional things or to ipipgo such a veteran service provider reliable, after all, their IP purity in the industry is notorious.