Data is at the heart of every machine learning model. Without it, model accuracy drops precipitously. However, there are many machine learning models that need to be developed without having as much data as the engineer would like. This is where data augmentation comes in. It is one of the primary ways of achieving model accuracy in production. However, it is quite a difficult practice to do, as it involves creating synthetic information to feed to your model.
There are not many frameworks currently available for this, which means that most engineers have to develop a solution from scratch. So, what is data augmentation?
What Is Data Augmentation
You can think of data augmentation as a process of creating synthetic data to feed into your machine learning models. For example, let’s say you are training an image recognition algorithm on a data set featuring cars. However, you might not have the necessary pictures of cars you need to feed that data set. Your model accuracy would drop quite low if you tried to put a model into production after training it on that data set.
A way you can alleviate that is to generate more pictures from the ones you already have. For example, you could have pictures of the same cars but different colors. You can also change their orientation by performing basic image manipulation algorithms on them. Doing simple things like this is what is called data augmentation.
How to Solve Data Augmentation Problems
One of the many use cases of data augmentation is when you are working on image classification algorithms. These algorithms need massive data sets to work well, but you might not have that on hand. Data augmentation solves this problem by easily helping you generate multiple versions of the same picture.
You might also be working with other classification algorithms that don’t have the necessary data set to perform adequately. It is always useful to have these tools on hand to help you do a better job with data augmentation. Sometimes it can be as simple as placing objects that shouldn’t be there in your machine learning model. The biggest benefit here is to model accuracy.
When Should You Use Data Augmentation In Your Machine Learning Process
There are a few major use cases for data augmentation in developing your machine learning models. When model accuracy is the most important thing, it should be one of the first methods you move towards to solve those problems. The next use case is when you have a small data set that needs a bit more to make your model as accurate as possible. By expanding your data set, you ensure that your AI algorithm has more to go on when training.
Finally, one of the other important areas is when you don’t have control over the input data. Real-world data might not be as clean and pristine as what you are working with in the lab to train your model. You can use data augmentation to create the rough data that your machine learning algorithm will see in production.