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.
Do you remember the saying, “For people who do not know where they are going, any path will take them there”? How things change! Today, it’s “No problem, let AI and machine learning figure it out!”. Artificial intelligence has advanced by leaps and bounds in recent years. Now, AI has beaten the world champion at Go, powers driverless cars, and carries out over two-thirds of all financial transactions in the world.
Smarter algorithms, more suitable programming languages, cheaper hardware, and more abundant data have all come together to create highly fertile conditions, especially for machine learning. The larger the amounts of data available for machine learning algorithms, the better the chances are of valuable and actionable insights. The web can be a particularly good source for that data, offering exponential growth, ongoing updates, and suitability for data mining.
Earlier AI efforts focused on rules and symbol manipulation. For applications like fraud detection, however, it became obvious that convoluted rule-based coding was expensive to develop and maintain, and not necessarily effective. Making use of the quantities of digital data that are doubling every 9 months, many of today’s AI initiatives are based on machine learning with data as a starting point instead of rules. Here’s how it plays out for the different kinds of learning:
The web is potentially one of the best data sources available, offering huge amounts of data that are constantly updated. Overall diversification is increasing, constantly broadening the relevance of web data. At the same time, sizable amounts of similarly structured data are available, streamlining ingestion into different machine learning applications.
Successful data extraction at scale then means meeting the following requirements:
Web data, like other big data, is also still the “heaviest” item in artificial intelligence. Although there are mountains of data available, it is not always easy to move around networks. Ideally, computation should go to the data, rather than trying to bring all the raw data to the place of computation. Data preparation may also take up to four times as much time as the machine learning process, while the time to process the data is another potential bottleneck. These constraints often lead to compromises such as using simpler machine learning classifiers that take less time to learn than more complex ones, but that offer less in the way of results.
Fortunately, there are solutions for these issues as well. Making cleansed, structured, web data available via an API helps enterprises to avoid shifting data around the web, reduces the time-consuming data preparation phase, and facilitates integration with the machine learning program. Meanwhile, there is continued progress in speeding up learning with more complex classifiers, thus letting enterprises make more of the big web data resources available.
As an example, you might use the following configuration of web datasets for a machine learning application to distinguish favorable comments and reviews from unfavorable ones, for a given product or service (sentiment classification):
The first two items can come from one overall dataset, split into two parts with 80% used for the training and 20% for the performance checking. A second, separate dataset can then be used for the final test.
You can also see a more detailed example in this initiative for detecting fake news using Webz.io news data, NLP and learning algorithms.
In general, the more data you have available to use, the more you can reduce uncertainty in the functioning of your machine learning program, whether for training or for deriving insights. Using web data, it also becomes more cost-efficient to use more data to better train machine learning programs or derive insights, than to slave over a hot algorithm to try to get better results.
When provided in clean, structured, machine-readable form, web data offers the volume and the diversity that AI needs to home in on consistently successful outcomes, and the convenience to bring AI and machine learning within reach of any organization, whatever its size or sector.
Start building smarter artificial intelligence apps today using our free machine learning datasets.
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.