How to Automate Supply Chain Risk Reports: A Guide for Developers
Do you use Python? If so, this guide will help you automate supply chain risk reports using AI Chat GPT and our News API.
So if RSS Crawlers are bad, Browser Scraping isn’t efficient, what about computer vision web-page analyzers? This technology uses machine learning and computer vision to extract information from web pages by interpreting pages visually as a human being might.
Computer vision crawlers present some great advantages over RSS/Browser or even code based crawlers. They offer simplicity when it comes to DIY crawlers, i.e letting non-developers teach the system what content needs to be extracted. In many cases it does a decent job at extracting structured content from sources it has no knowledge about.
So how am I going to ruin this one for you? Well it suffers from some of the downfalls of the browser bases crawlers:
Like with many machine learning systems, there is a precision and recall tradeoff, which means that if you want high precision (and you do), your recall will be low, which means that for many pages you won’t be able to extract the right content.
Computer vision crawlers are great for DIY-missions, for specific sites that look about the same, but I’m afraid not for large scale, precise crawling.
Do you use Python? If so, this guide will help you automate supply chain risk reports using AI Chat GPT and our News API.
Use this guide to learn how to easily automate supply chain risk reports with Chat GPT and news data.
A quick guide for developers to automate mergers and acquisitions reports with Python and AI. Learn to fetch data, analyze content, and generate reports automatically.