Machine learning (ML) and artificial intelligence (AI) appear to have caught the interest of automated data collecting companies. While web Scraping has been around for a while, AI/ML applications have only lately become visible to providers. Aleksandras ulenko, Product Owner at Oxylabs.io and a long-time user of these tools, presents his thoughts on the relevance of artificial intelligence, machine learning, and web Scraping.
AI/ML has a unique work-to-reward ratio. It can take months to write and construct a good model. You don’t actually have anything until then. Scraping or parsers, on the other hand, can take up to two days to complete. However, when you have an ML model, maintaining it takes a lot less time. As a result, there is always an option. You can create separate Scraping and parsers, but maintaining them will take a lot of time and work if they start piling up.
The second option is to have “nothing” for a long time, but then a wonderful answer that will save you a lot of time and effort later. There is a theoretical point at which custom solutions are no longer worthwhile. Regrettably, there isn’t a mathematical procedure for determining the proper answer. When all of the repetitious chores become too much of a drain on resources, you must make a decision.
However, getting started with machine learning is difficult. In terms of specialisation, it’s still a niche one. With other words, there aren’t many developers that dabble in machine learning, and given how difficult it is to find one in any subject, it’s a difficult river to cross. However, if the Scraping strategy is built on a long-term strategy, machine learning will undoubtedly come in handy in the future. Scaling is there in any good vision, and with scaling comes repetitive duties.