Artificial intelligence (AI) and machine learning (ML) seem to have piqued the interest of automated data collection providers. While web scraping has been around for some time, it is only recently that AI/ML implementations have seen business use.
AI/ML solutions and impact on development
AI/ML solutions have an interesting effort-payoff ratio with good models often taking months to write and develop, leaving you with nothing for extended periods. Dedicated scrapers or parsers, on the other hand, can be completed in just a day or two. However, when you have an ML model in place, maintaining it takes a lot less resources than the effort needed to keep dedicated scrapers and parsers up-to-date.
So, there’s always a choice when it comes to scrapers. The first option would be to build dedicated scrapers and parsers, which can take significant amounts of time and effort to maintain once they start stacking up. The other choice is to be patient and be without anything for an extended time, waiting for a brilliant solution to come through later on, saving considerable time, money and manpower in the long run.
It’s a question about picking short-term gain with a larger resource sink later on over long-term solutions with a complete resource sink at the start. Unfortunately, there’s no mathematical formula to definitively arrive at the correct answer. These decisions have to be decided internally, weighing the possibilities of the company.
Effect on deliverability and viability of projects
Getting started with machine learning is tough, especially when, comparatively speaking, it is a niche specialization. Ultimately, it’s rare to find many developers that dabble in machine learning, knowing how hard it can be to find developers for any single discipline.
Yet, if businesses tailor an approach to scraping based on a long-term vision, machine learning will become almost inevitable at some point. Every good vision has to scale, and with scaling comes repetitive tasks which are best handled by machine learning to ensure maximum efficiency.
It was once almost unthinkable that a machine learning model could be of such high benefit. Now the solution can deliver parsed results from a multitude of e-commerce product pages, irrespective of the changes between them or any that happen over time.
Making web scraping more user-friendly
Companies that have large IT departments are not immune to integration issues, as developers are almost always busy. Taking time out of their schedules for integration purposes is tough.
Additionally, departments that would need scraping the most, such as marketing and data analytics, might not have enough sway in deciding the roadmaps of developers. As such, even relatively small hurdles can become impactful. Therefore, scrapers should now be developed with a non-tech user in mind, so that developer input is needed less often.
Scraping software should include plenty of visuals that allow for simplified construction of workflows with a clear, easy-to-navigate dashboard to deliver information.
The future of scraping
Web scraping should always be examined with the future of the entire data acquisition industry in mind. Currently, it seems the position of scraping isn’t perfectly decided, with case law forming the basis of how we think about and approach web scraping. Due to this legal context, everything could change in a heartbeat, meaning that experts in the field should closely monitor developments and take steps to manage the situation accordingly.
Another potential development is that companies will realize the value of their data and start selling it on third-party marketplaces. Ultimately this would reduce the value of web scraping as a whole, as you could simply acquire what you need for a small price. After all, the data and insights are what businesses really need, with web scraping being a means to an end.
Furthermore, there’s a lot of potential in the grand vision of Web 3.0, which involves making the whole web interconnected and machine-readable. If this vision came to life, the whole data gathering landscape would be vastly transformed with the web becoming much easier to explore and organize. Parsing would become a thing of the past, and webmasters would get used to the idea of their data being consumed by non-human actors.
Crucially, user-friendliness will be a key focus of web scraping in the future. A significant part of obtaining data is exploration, finding where and how it’s stored and getting to it. Customers will often formulate an abstract request and developers will follow up with methods to acquire what is needed.
With the rise of AI and machine learning, the exploration phase will be much simpler, with users being able to take the abstract requests and turn them into something actionable through an interface. Ultimately, web scraping is breaking out of its shell of being something code-ridden or hard to understand and is evolving into a daily activity for everyone.
Author:
Aleksandras Šulženko, product owner at Oxylabs.io
You are in: Home » Guest Comment » GUEST COMMENT Tying artificial intelligence and web scraping together
GUEST COMMENT Tying artificial intelligence and web scraping together
by Aleksandras Šulženko
Artificial intelligence (AI) and machine learning (ML) seem to have piqued the interest of automated data collection providers. While web scraping has been around for some time, it is only recently that AI/ML implementations have seen business use.
AI/ML solutions and impact on development
AI/ML solutions have an interesting effort-payoff ratio with good models often taking months to write and develop, leaving you with nothing for extended periods. Dedicated scrapers or parsers, on the other hand, can be completed in just a day or two. However, when you have an ML model in place, maintaining it takes a lot less resources than the effort needed to keep dedicated scrapers and parsers up-to-date.
So, there’s always a choice when it comes to scrapers. The first option would be to build dedicated scrapers and parsers, which can take significant amounts of time and effort to maintain once they start stacking up. The other choice is to be patient and be without anything for an extended time, waiting for a brilliant solution to come through later on, saving considerable time, money and manpower in the long run.
It’s a question about picking short-term gain with a larger resource sink later on over long-term solutions with a complete resource sink at the start. Unfortunately, there’s no mathematical formula to definitively arrive at the correct answer. These decisions have to be decided internally, weighing the possibilities of the company.
Effect on deliverability and viability of projects
Getting started with machine learning is tough, especially when, comparatively speaking, it is a niche specialization. Ultimately, it’s rare to find many developers that dabble in machine learning, knowing how hard it can be to find developers for any single discipline.
Yet, if businesses tailor an approach to scraping based on a long-term vision, machine learning will become almost inevitable at some point. Every good vision has to scale, and with scaling comes repetitive tasks which are best handled by machine learning to ensure maximum efficiency.
It was once almost unthinkable that a machine learning model could be of such high benefit. Now the solution can deliver parsed results from a multitude of e-commerce product pages, irrespective of the changes between them or any that happen over time.
Making web scraping more user-friendly
Companies that have large IT departments are not immune to integration issues, as developers are almost always busy. Taking time out of their schedules for integration purposes is tough.
Additionally, departments that would need scraping the most, such as marketing and data analytics, might not have enough sway in deciding the roadmaps of developers. As such, even relatively small hurdles can become impactful. Therefore, scrapers should now be developed with a non-tech user in mind, so that developer input is needed less often.
Scraping software should include plenty of visuals that allow for simplified construction of workflows with a clear, easy-to-navigate dashboard to deliver information.
The future of scraping
Web scraping should always be examined with the future of the entire data acquisition industry in mind. Currently, it seems the position of scraping isn’t perfectly decided, with case law forming the basis of how we think about and approach web scraping. Due to this legal context, everything could change in a heartbeat, meaning that experts in the field should closely monitor developments and take steps to manage the situation accordingly.
Another potential development is that companies will realize the value of their data and start selling it on third-party marketplaces. Ultimately this would reduce the value of web scraping as a whole, as you could simply acquire what you need for a small price. After all, the data and insights are what businesses really need, with web scraping being a means to an end.
Furthermore, there’s a lot of potential in the grand vision of Web 3.0, which involves making the whole web interconnected and machine-readable. If this vision came to life, the whole data gathering landscape would be vastly transformed with the web becoming much easier to explore and organize. Parsing would become a thing of the past, and webmasters would get used to the idea of their data being consumed by non-human actors.
Crucially, user-friendliness will be a key focus of web scraping in the future. A significant part of obtaining data is exploration, finding where and how it’s stored and getting to it. Customers will often formulate an abstract request and developers will follow up with methods to acquire what is needed.
With the rise of AI and machine learning, the exploration phase will be much simpler, with users being able to take the abstract requests and turn them into something actionable through an interface. Ultimately, web scraping is breaking out of its shell of being something code-ridden or hard to understand and is evolving into a daily activity for everyone.
Author:
Aleksandras Šulženko, product owner at Oxylabs.io
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