Structured data, the type of data that fits into predefined formats like spreadsheets, is easy for a machine to process. By contrast, unstructured data comes in a “raw” form and can include social media posts, images, audio recordings, videos, emails, and PDFs.
Many AI developments in recent years, such as natural language processing and computer vision, aim to parse unstructured data sets for use in model training. Leading machines to process unstructured data effectively will result in significant value for businesses, as 80% of data will be in an unstructured format by 2025.
Machine learning algorithms for unstructured data have already seen use in real-world applications. Natural language processing provides speech recognition capabilities for customer service phone lines, and self-driving vehicles use computer vision to recognize features of the road.
Why Machine Learning Has Struggled with Unstructured Data Types
Part of the reason why AI development has primarily focused on structured data sets up until now is that machines process structured data more easily.
Machines can use a combination of formulas and statistical analyses to predict financial activity in future markets, but programmers can’t hard-code a list of rules that reliably tell an algorithm what an image of a ferris wheel looks like.
Building AI To Deal With Unstructured Data
Only relatively recently have machine learning models started working with unstructured data types at scale, and many developers are picking up best practices to allow these models to work with more than just spreadsheet entries.
The following considerations are for working with unstructured data in machine learning.
It’s already good programming practice to avoid deleting older versions of your code, and it’s especially necessary with model training.
For example, if you deploy a model several years ago and only recently discovered mistakes it makes regarding edge cases, you must go back to the original training data set and code you had back then.
Versioning also helps companies stay compliant with auditing requirements, which sometimes require developers to modify the original dataset and retrain their models. Keeping older versions of your work ensures you won’t have to start from scratch every time.
And the most significant issue facing machine learning is model drift. As new data changes, it’s possible for the model’s old prediction mechanisms trained on historical data to be insufficient. Developers will likely have to retrain with new sets, and versioning allows them to make incremental adjustments.
Unstructured data is rarely neatly organized, which makes data quality assurance even more necessary. AI models rely on the quality of their input data, and data management includes tasks like categorizing data points, eliminating redundant or erroneous entries, and labeling the data to help the algorithm make its predictions.
Poor quality data is a potential cause for poor model performance. If multiple models generate the same incorrect predictions from the same data set, the issue likely has to do with the quality of your input data.
Take computer vision for autonomous vehicles as an example, which uses unstructured images and video files to parse features on the road. Car companies use data labeling services to pinpoint the locations of cars and road signs, but exactly where the bounding box belongs can be fairly subjective. Where does it end if the object is obstructed, for example?
Data management aims to make data more consistent and useful for model training, and it’s especially vital for models working with unstructured data sets.
Another barrier to adoption is the need for unstructured databases. A data set with potentially millions of images for a machine learning algorithm to parse would take far too long for a traditional file system to handle, so many developers are moving unstructured data into databases, a new architecture that changes the way we store and process data.