Custom AI model development: training AI models on proxy data

When AI model meets proxy IP, this is the way to play it.

Recently, a number of AI development of the old iron to find me complaining, said that their own training model is always like a fool - recognition of pictures of huskies recognized as wolves, semantic analysis can not distinguish between "okay" is positive or negative. If you ask me, the data has to take the blame. Today, let's nag how to use proxy IP to feed the model some "grains and cereals".

First, why do you have to use a proxy IP to raise a model?

To give a real example: last year, an e-commerce platform than the price of robot jokes, the price of mutton in Inner Mongolia and Hainan's coconut price to put a piece of comparison. Why?All IPs are crowded in Hangzhou server room during data collection, the site automatically blocked the abnormal traffic. This is like letting a child who eats takeout all the time learn how to make a full-course meal, can it be reliable?

With ipipgo's Dynamic Residential Proxy, every request is sent from the real user's network. It's like having a purchasing agent planted all over the country, and getting price data that's called real. Their TK line is particularly suitable for cross-border data, before helping friends do Southeast Asia market forecasting model, with this program to save 30% data cleaning time.

Second, the data collection practical three axes

The first move: IP rotation should be like a Sichuan opera face change

import requests
from ipipgo import get_proxy ipipgo official SDK

def crawler(url): { url = { url = { url = { url = { url = { url
    proxies = {
        "http": get_proxy(type='dynamic'),
        "https": get_proxy(type='dynamic')
    }
    response = requests.get(url, proxies=proxies)
    return response.text

Watch this.type parameterDynamic residential suitable for regular collection, if you run into a hard-core site (talking about a certain East and a certain treasure), you have to cut to the static residential package, 35 dollars / IP / month that.

Tip #2: Request frequency has to be learned from an old Chinese doctor taking a pulse

Don't go all violent crawler and play with the web server. It is recommended to set the frequency this way:

III. IP management in model training

The most tigerish operation I've ever seen is to take 500 IPs and binge scan the job boards at the same time, resulting in the model confusing the job requirements with the matchmaking conditions. The correct approach is:

1. Geographical distribution: using ipipgo'sCountry-City-OperatorTertiary targeting, e.g., doing used car valuation modeling, focusing on capturing agent IPs in Tier 1 and 2 cities

2. Protocol selection: do not stick to HTTP, some APP data with Socks5 protocol better catch, just ipipgo full support!

3. Exception handling: don't panic when encountering CAPTCHA, their API return status code is very full, 1024 means that the IP is limited, hurry up and change the next!

IV. QA time for veteran drivers

Q: What should I do if my IP is blocked?
A: First of all, see if the use of static IP package, dynamic IP would have been automatically replaced. If you are an enterprise-level user, you can directly look for ipipgo's technical staff to adjust your IP address.cross-border rail line, that line is solid as hell.

Q: Which package should I choose when I first start modeling?
A: In good conscience, first on the dynamic standard version, 7.67 yuan / GB enough to play for a month. Wait for the model to run through before upgrading, don't learn from some rash people to come up and buy the most expensive.

Q: What if I have to interface with multiple data sources?
A: Their houseCloud Server + Proxy IPThe package can be tried, the data directly go to the intranet transmission, much faster than the public network crawling. Last time for a MCN to do net red influence model, with this program to save 60% time.

In the end, raising AI models is like raising a baby, and data is milk powder. Use the right proxy IP is equivalent to give the baby to eat organic vegetables, although a little effort, but grow up quasi-guaranteed to be smarter than eating hormones. Recently saw ipipgo out of theSERP API, specializing in search engine data collection, do NLP model of the old iron can go to try the water.