The best data sources on the web for training specific AI models

Why your data sources matter

Picture this: The best water treatment system in the world but the water source is a chemical waste dump. These chemicals are particularly stubborn — there’s no way to actually remove them from the drinking water. They still seep in and poison the town. The only solution in this scenario is to choose a different water source.

The same can be said for AI training data. Every AI model begins with its data. Better algorithms do help — but in our data lies the source of everything — just like a drinking water system. The internet contains so much data, we have an entire industry built around deciding which data is worthwhile and how to interpret it.

In this guide, we’ll look at how you can source your data based on project needs. We’ll help you understand the following types of training data and where to obtain them.

• Text and Natural Language Processing (NLP) data
• Computer vision and image model data
• Audio and speech data
• Video data
• Structured and tabular data

Choosing the right data source for your AI model

Not all AI projects use the same data sources. A facial recognition model doesn’t need to understand deep inferences across literary sources. It needs to scan a face and then recognize it in other images.

Your training data should reflect the model’s actual purpose.

Here are three key things you should think about when choosing a data source.

• Relevance: Does the data reflect real-world patterns? If so, will these patterns help the model to fulfill its intended use cases?
• Quality: What’s the data structure like? How much cleaning is involved? Will there be balancing or augmentation required? How much data curation will you require?
• Scale: How large is the dataset? Will it scale? Does the dataset reflect smaller nuances not seen in synthetic data?

Open web datasets for general-purpose AI models

General purpose AI models are by far the ones humans notice every day. When you ask your digital assistant to analyze a picture, generate an image or to respond to a prompt, it needs a solid basic understanding of all these data types.

When you’re looking for diverse data sources — NLP, computer vision, audio and video — the internet can provide you with all of these. There are a number of places you can obtain these datasets as well.

Free sources

• Common Crawl: A publicly available repository containing petabytes of scraped data. Datasets are incredibly large and diverse but uncurated. You’ll need to preprocess and annotate all of it before training.
• LAION: A vast repository containing data for image-text pairs, 3D modeling and audio data. You can build a solid general purpose AI using sources from LAION alone.
• Hugging Face: One of the world’s biggest hubs for AI models and training data. They offer a plethora of curated datasets and you can even download a pretrained foundational model to skip pretraining entirely.

Commercial sources

• Bright Data: Pull any snapshot from Bright Data’s petabyte-sized archives. They also offer tailored datasets with strong typing in a variety of formats.
• AWS Data Exchange: A large marketplace where teams and providers of all kinds can exchange data.
• Snowflake Marketplace: Get your hands on AI-ready datasets integrated directly into the snowflake platform.

Most free and commercial providers aren’t limited to one type of data. In fact, the sources listed above have offerings available for all the types of data we’ll discuss throughout the rest of this article.

Text and NLP training sources

From chatbots to search engines, almost all modern AI you interact with is based on text and NLP data. Not all text is created equal. Different texts serve different purposes. You wouldn’t train a customer assistant on the latest news articles, you’d use how-to manuals and other technical sources. A model trained on Reddit slang is going to be a bad fit for writing whitepapers and academic research.

When sourcing your NLP data, think of what it imparts to the following layers of your model.

• Foundation: Large, diverse conversational datasets provide your model with semantic understanding of diverse inputs. A model trained on everybody’s language can converse with everybody.
• Domain expertise: What sort of expertise does your model gain from the data? If you train a grammar assistant entirely on emojis and Gen Alpha slang, you’re going to experience problems.
• Conversational balance: When training on heavy dialogue like you’d see from Reddit and StackOverflow, your model is able to handle longform conversation without flaking out.

The real downsides to NLP and text data seep in through bias and noise. We’ve all heard the horror stories of bots trained on social media. Human conversation can impart human biases to your AI model. It’s up to you to guard against harmful bias.

Vision and image model sources

Computer vision is one of the most overlooked types of AI in the industry. When most of us think of AI, we think of chatbots trained to generate content. Vision models are trained to recognize things in the environment. This powers everything from self-driving cars to AI-powered vacuums and even medical imaging.

In computer vision, the quality of your dataset can literally make the difference between life and death. When evaluating image datasets, we need to keep the following angles at the forefront.

• Diversity of perspective: Imagine a model trained only on bright, high-quality, high-resolution images. Now put it behind the wheel of a car in the middle of a stormy night. Without diversity of training data, models are unable to handle the most dangerous of scenarios.
• Annotation: Annotation and labeling are the bridge between the image itself and the patterns recognized by the model. If a facial recognition system doesn’t account for frowns and smiles, it’s broken from the start.
• Domain-specific training: Autonomous vacuums need to recognize things they’d see in houses — cats, dogs, couches and what not. If the vacuum has been trained on horses and barns, this isn’t going to help it perform its task — unless you keep these things in your house.

Bias and lack of diversity present two of the biggest challenges in computer vision. A decade ago, facial recognition systems were notorious for working perfectly with people of one demographic while throwing false positives on others. Algorithms are now much more improved, but history shows us what bad data can do to a computer vision model.

Audio and speech sources

Audio and speech platforms add another dimension of complexity. Inventors and engineers have been attempting speech recognition systems since the late 1800s — long before the age of digital algorithms. Even today, these systems present unique challenges. Not only does a model need to analyze data, it needs to analyze data in real-time. Companies like Soniox specialize in speech data for AI.

“Ice cream” and “I scream” have incredibly different meanings. In spoken language, their definitive difference is a micropause, only a small fraction of a second. Models need to analyze phonetics while taking time into account to handle these subtle differences.

For your model to truly understand what it’s listening to, your dataset should reflect the following needs.

• Speaker diversity: AI models need to understand different accents and dialects. If you train your model entirely on “the queen’s English”, it’s going to misunderstand the rest of the English speaking world. The same can be said for any language.
• Background noise and realism: We’ve all been there. Sitting on the phone with a customer service agent. Dogs are barking. Kids are screaming. Even a human can’t understand half of what you’re saying. Well trained models can listen to your voice even in this sort of environment.
• Annotation and transcription: Just like other sources we’ve mentioned before, annotation is paramount. If a model can’t pick up on a customer’s frustrated tone, the situation escalates.

Speech recognition models present some of the hardest problems to solve in AI. Today, our tech is better than it’s ever been. However, age old problems still tend to rear their ugly heads when models are trained on poor data.

Video datasets

Take the challenges in image and audio datasets. Now combine them. Your model no longer needs to analyze a single picture. It doesn’t need to simply listen to an audio stream. Now, it needs to analyze a stream of pictures, often 30 or 60 frames per second (FPS) while analyzing an audio stream simultaneously.

Video datasets are some of the heaviest and most complex datasets in the AI industry today. Here are key pieces to look for in video datasets.

• Temporal understanding: Imagine a simple clip of a dog running around the yard and barking. The model needs to understand that the dog in this image (in a completely different location than he was two seconds ago) is still the same dog. It needs to understand that in the concurrent audio stream, the barking sound is coming from that same dog too.
• Context and continuity: Perhaps at the beginning of the clip, a person threw a tennis ball. The model needs to recognize the cause and effect at play here. While handling concurrent data streams, the model should understand: “Human throws ball. Dog barks and plays.” The best models might even conclude, “this is a game that humans play together with dogs.”
• Real-world variability: There are thousands of different dog breeds. Tennis balls aren’t limited to a single brand. No backyard looks exactly the same. The model needs to understand all of these things at a conceptual level. It should recognize people, dogs, tennis balls and backyards of all kinds.

Video at scale presents incredible difficulties. Have you ever recorded a video? Prolonged recordings can easily eat up a gigabyte of space. Now, write a CSV file that provides second by second (often even smaller chunks) explanation both data streams involved. Annotation of video data is incredibly tedious and expensive. Bright Data and Appen both offer solutions for AI-ready video data.

Structured and tabular sources

Not all models use text, image or audio data. Some of the most important models today rely entirely on tabular data. Tabular sources are simple but at scale, they grow in complexity and reveal patterns most of us would never imagine — this is why analysts and data scientists are so highly sought after.

With structured datasets, keep these points in mind.

• Consistency: Context and structure remain the same across the dataset. Imagine a dataset containing people. For the first two thousand rows, people named “Jacob” were entered as such. Then, someone started inputting “Jake.” The model sees two different names. In a CSV file, even a missed comma can shift the entire set.
• Completeness: Empty rows and columns should be removed entirely. Any missing cells should be filled with placeholders — something to signal that the data was unavailable — or even dropped entirely. Incomplete data is a major cause of bias and malformed pattern recognition.
• Relevance: Don’t use data just because it’s available. This data is often used to train prediction models. A financial forecasting model doesn’t need to train on medical data, it dilutes expertise and bloats the model.

Structured and tabular data sources need to be verified and often cleaned before annotation and eventual training. Annotations need to be relevant as well. Irrelevant columns leads to irrelevant inferences, which is exactly what you want to combat.

Conclusion

AI models are only as strong as their training data. Whatever AI system you’re building, choose your data sources wisely. When your model trains on a flawed dataset, it sees the world in a flawed way. There’s a reason training data is also called foundational data. Everything else relies on this data.

The internet is overflowing with both free and commercial data sources. You don’t need to worry about finding data. You need to choose the right data for your use case. Make sure it’s clean. Make sure it’s relevant and make sure it represents the real-world patterns you want it to reflect. Failed model training wastes time and resources just to wind up right back at square one.