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The last few years have seen a surge in discussions around artificial intelligence (AI) and machine learning. Today, the spike seems even more unprecedented. These technologies are now used by millions of people around the world, from performing textual tasks to creating images and making music.
Quality data plays a crucial role in AI learning, allowing accurate predictions, insights, and decision-making. However, large-scale information gathering is not easy – it requires technical knowledge to bypass restrictions applied by websites. That is where proxies come in. A proxy server helps mask the user’s real IP address and location, overcoming most challenges when collecting bulk data for AI training.
This article will guide you through web scraping for AI, the benefits of automated data collection, and the role of quality proxy services based on Proxyway’s annual Proxy Market Research data.
A Forbes Advisor study shows that 56% of companies use AI to improve business operations. Some of the use cases include training advanced Q&A capabilities, building chatbots with conversational data, creating enterprise search, or enhancing custom image recognition.
AI training and machine learning require a lot of quality data. Such systems work by combining large sets of information with algorithms and learning from the data they analyze. While data licensing is emerging as a profitable model for some platforms, most of the data still comes from web scraping.
Web scraping refers to the process of collecting data from the web using automated tools such as third-party software or custom-built scripts. It can speed up information-gathering tasks: automated data collection takes just a few minutes, rather than manually browsing multiple websites to gather data over several days.
According to Proxy Market Research, major proxy server and web data providers mentioned that AI-related use cases have become common. One company even reported that AI has become its dominant sector – it grew by 430% year over year and accounted for over 50% of the user base.
AI-based decisions allow businesses to make fast and accurate decisions by training AI models using web scraping. Here's how automated data collection can benefit companies:
Diverse data. Web scraping allows AI models to gather diverse and extensive data from various sources. This can help to improve the model's understanding of different contexts, languages, and topics. Also, businesses can collect large amounts of data quickly.
Real-time updates. Information today can get old really quickly. Automated data collection provides uninterrupted access to target information. Users can continuously monitor websites and get information as it gets produced.
Customized data. Companies can get data from different industries, such as e-commerce, social media platforms, search engines, and others. The variety of data businesses collect to train AI models differs. Some scrape text, while others use images or videos.
In essence, web scraping gives quality data for training AI models, leading to better performance and more accurate predictions in different industries.
Web scraping becomes more challenging every year. Websites apply security measures to prevent gathering competitive intelligence, stop bad-bot traffic, or simply reduce server load.
Automated data collection is difficult with a single IP address because websites limit the number of requests a user can make. A proxy server acts as an intermediary between the user and the target website. It changes the perceived IP and location to a new one, making it hard for websites to detect bot-like activities. However, not all proxies are equally good or suitable for web scraping.
Proxy servers are usually categorized by their IP source. Two main proxy types used for web scraping are residential and rotating datacenter. You can also go for ISP or mobile proxies, but they are limited to specific web scraping use cases.
Rotating datacenter proxies are associated with cloud hosting companies like Amazon Cloud Services (AWS) and are hosted on servers in data centers. They run on a fast internet connection, so businesses can access vast amounts of data very quickly.
However, websites see datacenter IPs as belonging to hosting companies, so they are easy to detect. Datacenter proxies work best with websites that don’t apply rigorous anti-scraping measures.
Residential proxies originate from real people’s devices connected to Wi-Fi, such as phones, laptops, and TVs. These IPs come with a large pool of addresses and they are issued by an internet service provider rather than a cloud hosting company like Amazon’s AWS.
Residential proxies are less predictable than their datacenter counterparts because they rely on the end user's connection. The proxies rotate with every connection request, so whenever the user sends a request to the target website, it appears as a new person. Websites can not distinguish the source of the IP address because the user looks like a regular visitor.
Regarding datacenter proxies, major companies believe these addresses are no longer suitable for popular targets like Amazon, Google, or social media because of tightening bot protection. However, they work almost without fail with websites that don’t use rigorous security measures.
Residential proxies come from very large proxy pools. The biggest advertised pool has grown from 100M to 155M proxies in just a year. Residential addresses cover many locations around the world with city and ASN targeting. Some providers even offer ZIP code targeting.
Rotating datacenter proxies are nearly always online because they are hosted in data centers. However, these networks offer fewer locations because creating datacenter proxies in different locations requires servers there, which is expensive. The number of available IPs ranges from 10K to 230K addresses in up to 90 locations.
Infrastructure performance.
Even though residential proxies depend on the connection of end users, these networks work well, promising very few connection errors. Over the years, major residential networks have improved their infrastructure performance so that now they can measure up to datacenter addresses in terms of success rate.
When talking about response time, residential proxy providers are getting faster every year. But there’s no way to match datacenter proxies because they use very fast internet connections.
Performance with Amazon.
It’s no surprise that Amazon is the most desirable target. It is the largest e-retailer in the world and holds a lot of valuable data like pricing and product information.
The range of anti-bot strategies websites use varies. Amazon applies in-house CAPTCHA and returns empty 200-coded responses – a user thinks that the scrape was successful while, in reality, no data is returned.
Both residential and datacenter proxies were slow to open Amazon, mainly due to its size. However, rotating datacenter IPs were 1.7 times as fast.
Residential proxies are charged by traffic. Subscription is the dominant pricing model, but users can pay as they go with most proxy service providers.
Last year brought many changes in terms of pricing; at least seven major proxy providers took price cuts ranging from 10% to 55%. The lowest entry price now starts at $7, and the median at $8.4.
Some rotating datacenter proxy providers charge by traffic, and others by both IPs and traffic. The cheapest price is as low as $0.65. Compared to residential proxies, rotating datacenter proxy servers cost up to 7 times less, depending on how much you buy.
In conclusion, when it comes to choosing proxies for AI training, the choice between residential and rotating datacenter proxies depends on various factors such as target websites' security measures, performance requirements, and budget considerations. Residential addresses work better with well-protected targets like Amazon, but they are slower. Datacenter IPs, on the other hand, are faster, but websites can easily detect them.
