Custom Data Training AI: Proxy ip data collection to train AI models

When AI meets proxy IP: data training can still be played this way

Recently, I was jerking off with some algorithmic buddies and talked about how the biggest headache they have in training AI models is theInsufficient data diversity. There is an e-commerce price comparison buddies spit out: "platform anti-climbing upgraded, collecting data is more difficult than the sky!" At this time I silently pulled out my cell phone to show him the background of ipipgo - good guy, his eyes directly light.

The three lifebloods of real data collection

Nowadays, doing data collection is like fighting a guerrilla war, and you must master the three main rules of survival:

 Practical case: e-commerce price monitoring
import requests
from ipipgo import get_proxy Use ipipgo's SDK here.

def crawl_product(url).
    proxy = get_proxy(type='dynamic') dynamic residential IP rotation
    try.
        res = requests.get(url, proxies={'https': proxy}, timeout=10)
         Data parsing logic...
    except Exception as e.
        print(f "Capture failed to switch IP automatically: {e}")

The code looks simple, but hides two key points:Dynamic IP automatic switching mechanismrespond in singingAutomatic retry after exception handling.. With ipipgo's Dynamic Residential package, the $7.67/GB price is especially friendly to startup teams.

The Hidden Levels of Data Cleaning

The data collected is like unpanned sands that have to be processed with these three axes:

Especially do LBS service brother to pay attention to, last time a do takeaway analysis team, because did not clean IP geographical characteristics, the model of the milk tea store in Sanya recommended to Harbin...

Practical tips for model training

Here's a real-life example: the training process of a content review AI

 IP dimension processing in feature engineering
def process_features(data).
     Extract IP country/carrier features
    geo_info = ipipgo.lookup(data['ip'])
    data['is_mobile_network'] = geo_info['carrier type'] == 'mobile'
     Time zone feature alignment...

Through ipipgo's IP resolution interface, it can extract 20+ dimensions of network environment features. There is a team doing advertising anti-fraud, and the model accuracy went up 18% directly after adding these features.

Frequently Asked Questions

Q: Why train AI with proxy IP?
A: Just as people can't stay in one city to see the world, AI needs data from multiple networked environments in order not to be easily "biased".

Q: What's special about Enterprise Dynamic IP?
A: It's like the difference between an ordinary bus and a specialized business bus. Enterprise package with exclusive IP pool and QoS guarantee, $9.47/GB is suitable for high-frequency demand.

Q: Does data cleansing have to be done manually?
A: It is recommended to use automated scripts + manual sampling, ipipgo's API returns structured data, which can save 80% cleaning time

Recently found a new way to play: use ipipgo's cross-border line to collect multi-language data, with a large model to do real-time translation training. There is a team relying on this to expand language support from 3 to 12 languages in three months, this wave of operation is really 666.