AI models are working on almost everything from medical diagnoses to fraud detection in banks. For these models to be effective, they need to be trained to do their job. Training AI models requires very high volumes of good-quality data. The dataset varies based on the model you’re creating, but it has to be diverse and unbiased. So where do you find it? Let’s run through five common ways to get your hands on training data and explore the pros and cons of each.
Open-source datasets
An open-source data set is a collection of data that’s freely available to the public. Providers place few limitations on access, modification, and sharing rights.
- Examples: You can get free-to-use datasets from Google’s Datasets Search Engine, Microsoft, UCI Machine Learning Repository, Kaggle, Amazon, and more.
- The pros: Free datasets are fast and easy to acquire. They may contain rich and detailed data, and they’re cost-efficient too. You may be able to find pre-processed datasets that fit your needs without much effort.
- The cons: The problem is that the data is usually not original. You may end up with overused, generic data. If you’re building a unique model, finding free datasets that fit your needs can be tricky.
Web data (Scraping)
Web scraping tools allow you to collect large volumes of public information from a variety of websites. For example, if you’re building a sentiment analysis tool, you’ll benefit from public social media posts, reviews, and discussion threads from people talking about products or services they’ve used.
Examples: Scrapy, ParseHub, ScrapingBot, ProWebScraper, Dexi, ScraperAPI, and WebScraper are just a few of the scraping tools you can use to create a dataset.
The pros: The big advantage is control. You can target exactly what matters to your project and get really specific with your dataset.
The cons: Scraping can get messy. Some sites block bots. Others have strict terms of use. Even when you get the data, it might be in twenty different formats, full of errors, and missing pieces.
Purchase a dataset
Sometimes it makes sense to simply pay for what you need. There are companies that sell specialized datasets, often already cleaned and labeled. You might find anything from medical imaging libraries to curated financial transaction records.
Examples: You can buy high-quality datasets from Bright Data, Datarade, Coresignal, Statista, Data & Sons, and many other providers.
The pros: The benefits of buying datasets upfront are speed and ease of access. You skip months of collection work.
The cons: The risk is that you are relying on someone else’s idea of quality. And once you buy it, you still have to make sure it actually fits your model’s needs.
Synthetic datasets
Synthetic datasets are not based on real human data. They’re artificially created using computer programs and designed to replicate authentic data. Synthetic datasets can come in handy when real data is too sensitive or difficult to obtain (think medical records or financial information).
Examples: Generate your own synthetic data using generative AI tools, rules engines (create artificial data based on established rules), or entity cloning (existing data is altered to create new, unique instances). You can also purchase synthetic data from third-party providers.
The pros: Synthetic data frees you from risks related to copyright infringement, privacy, and compliance. It’s a useful solution when you can’t find the real-world data you’re looking for.
The cons: The downside is that creating synthetic data can be a massive effort for small teams. You also run the risk of creating a biased dataset or facing model collapse.
If none of these methods work for you, you can also collect your own data. This approach is labor-intensive; it involves setting up sensors, building a survey, or running a mobile app that gathers input from users. You’ll also have to label the data. The process is slow, but you end up with a dataset no one else has.
There’s no single best way to get training data for an AI model. Each approach involves certain tradeoffs. Most successful projects combine several sources, testing, and refining as they go. The better your data, the better your model will be.
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