Buyer’s Guide for
AI Training Data
What is AI Training Data?
AI training data is carefully curated and cleaned information that is fed into a system for training purposes. This process makes or breaks an AI model’s success. It can help in developing the understanding that not all four-legged animals in an image are dogs or it could help a model differentiate between angry yelling and joyous laughter. It is the first stage in building artificial intelligence modules that requires spoon-feeding data to teach machines the basics and enable them to learn as more data is fed. This, again, makes way for an efficient module that churns out precise results to end users.
Consider an AI training data process as a practice session for a musician, where the more they practice, the better they get at a song or a scale. The only difference here is that machines have to also first be taught what a music instrument is. Similar to the musician who makes good use of the countless hours spent on practice on stage, an AI model offers optimum experience to consumers when deployed.
Why is AI Training Data Required?
Let’s consider the example of autonomous cars again. Terabytes after terabytes of data in a self-driving vehicle comes from multiple sensors, computer vision devices, RADAR, LIDARs and much more. All these massive chunks of data would be pointless if the central processing system of the car does not know what to do with it.
For instance, the computer vision unit of the car could be spewing volumes of data on road elements such as pedestrians, animals, potholes and more. If the machine learning module is not trained to identify them, the vehicle would not know that they are hindrances that could cause accidents if encountered. That’s why the modules have to be trained on what every single element in the road is and how different driving decisions are required for each one.
While this is just for visual elements, the car should also be able to understand human instructions through Natural Language Processing (NLP) and audio or speech collection and respond accordingly. For instance, if the driver commands the in-car infotainment system to look for gas stations nearby, it should be able to understand the requirement and throw appropriate results. For that, however, it should be able to understand every single word in the phrase, connect them and be able to understand the question.
While you could wonder if the process of AI training data is complex only because it is deployed for a heavy use case such as an autonomous car, the fact is even the next movie Netflix recommends goes through the same process to offer you personalized suggestions. Any app, platform or an entity that has AI associated with it is by default powered by AI training data.
The simplest answer to why AI training data is required for a model’s development is that without it machines wouldn’t even know what to comprehend in the first place. Like an individual trained for their particular job, a machine needs a corpus of information to serve a specific purpose and deliver corresponding results, as well.
Apart from this, what also influences the amount of data required for training includes aspects listed below:
- Training method, where the differences in data types (structured and unstructured) influence the need for volumes of data
- data annotation or labeling techniques
- the way data is fed to a system
- error tolerance quotient, which simply means the percentage of errors that is negligible in your niche or domain
Real-world Examples of Training Volumes
Though the amount of data you need to train your modules depends on your project and the other factors we discussed earlier, a little inspiration or reference would help get an extensive idea on data requirements.
The following are real-world examples of the amount of datasets used for AI training purposes by diverse companies and businesses.
- Facial recognition – a sample size of over 450,000 facial images
- Image annotation – a sample size of over 185,000 images with close to 650,000 annotated objects
- Facebook sentiment analysis – a sample size of over 9,000 comments and 62,000 posts
- Chatbot training – a sample size of over 200,000 questions with over 2 million answers
- Translation app – a sample size of over 300,000 audio or speech collection from non-native speakers
Where do you source AI Training Data from?
Let’s explore them individually.
Free sources are avenues that are involuntary repositories of massive volumes of data. It is data that is simply lying there on the surface for free. Some of the free resources include –
- Google datasets, where over 250 million sets of data were released in 2020
- Forums like Reddit, Quora and more, which are resourceful sources for data. Besides, data science and AI communities in these forums could also help you with particular data sets when reached out.
- Kaggle is another free source where you can find machine learning resources apart from free data sets.
- We have also listed free open datasets to get you started with training your AI models
While these avenues are free, what you would end up spending are time and effort. Data from free sources is all over the place and you have to put in hours of work into sourcing, cleaning and tailoring it to suit your needs.
One of the other important pointers to remember is that some of the data from free sources cannot be used for commercial purposes as well. It requires data licencing.
Unlike our previous section, we have a very precise insight here. For those of you looking to source data or if you are in the process of video collection, image collection, text collection and more, there are three primary avenues you can source your data from.