There is no concept of data overload for artificial intelligence (AI). The more information you have, the better. The demand for data continues to grow since AI data annotation systems can handle massive volumes of data, and their accuracy improves as data volume increases.
Although data collection appears to be the most superficial phase in the ML process, it necessitates the identification of acceptable databases. As a result, the most typical data sources for an ML model are:
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1. Open-Source Datasets
Using an open-source dataset to collect data for your ML model is the simplest and fastest way to do it. Thousands of open-source datasets are available on the internet, just like coding snippets. They are easy to find, free to use, and incredibly time-efficient to utilize.
One disadvantage of using public datasets is that, while they may appear to have an infinite amount of rich, detailed data, you will almost certainly need to clean it to meet your specific goals.
Some examples of open-source datasets include:
- Kaggle
- Amazon
- UCI Machine Learning Repository
- Google’s Datasets Search Engine
- Microsoft
- Government Datasets
- Lionbridge AI
2. Web Scraping
Web scraping programs are used to retrieve data from Amazon, such as product descriptions and prices. These programs automatically or manually hunt for new data, acquiring the new or updated data and saving it for quick access.
There are plenty of excellent web scraping solutions available. Some of these applications involve coding, while others do not. Some are free and open-source, while others are not.
If you decide to utilize web scraping to obtain data, ensure you can collect the information you want. It’s not worth getting into legal problems with a machine learning model. The following are some web scraping tools:
- Scrapy
- ProWebScraper
- ScraperAPI
3. Synthetic Datasets
When appropriate real-world data is unavailable, synthetic datasets come in handy (or is very hard to obtain). It aids in defining numerous features inside the dataset, such as the scope, format, and level of noise. It also eliminates the possibility of copyright infringement or privacy concerns. It is beneficial if your dataset requires any personally identifying information.
4. Manual Data Generation
Crowdsourcing is used for manual data collection. Human workers are assigned jobs to collect the appropriate data bits, which are then combined to form the resulting dataset. Crowdsourcing projects range from simple tasks such as image labelling to more sophisticated jobs such as collaborative writing that require numerous phases.
Amazon Mechanical Turk is the most popular crowdsourcing platform, where tasks are allocated to human workers who are paid for completing them.
Things to remember:
In any machine learning task, a suitable dataset is essential. Therefore, before you begin the collection, keep the following in mind.
- A clean dataset with few outliers and noise is preferable.
- Conduct a thorough literature review of the problem you’re trying to solve. It is to ensure that the relevant (or appropriate, helpful, or discriminative) features (or variables or predictors) that you collect are the most critical part of a machine learning work.
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