Data management takes up to 80% of the time spent on any Machine Learning project. From collecting data to converting them to structured, usable data, it is the most hectic task in machine learning.
What is Data Labeling?
Data labeling is the art of organizing raw data into a simpler and meaningful form. Be it a simple project for face detection or other complex programs, like real-time object detection, and it is unavoidable. The better the labeling, the better will be the AI predictions. But again, what is data labeling in machine learning?
Data labeling uses unstructured data as input to make something meaningful out of it. However, annotation, classification & tagging is what data labelling does. This data is used to train a machine-learning algorithm. In this process, called model training, the algorithm extracts information from the labelled data. This lays the foundation for the algorithm to detect faces or do even complex predictions when deployed on real-time scenarios, after training. So, labelling data is important as it directly impacts the performance of your algorithm.
Methods of labeling data | Data Classification and Labeling
The bigger and accurate your training data is, the better the AI will perform in making predictions. So, it is non-negotiable that one has to be accurate during data classification and labeling. However, a larger dataset needs better data management. And that is why there are different methods to label your dataset.
You can use your staff to label the dataset. Even though you don’t have to find more workforce, you have to train them for the process. Thus, this method is not apt for larger projects.
Utilizing freelancers to label data is called outsourcing. However, there are privacy concerns regarding this method as you don’t know the employee you are working with. Besides, even this method is not as efficient as the data grows.
Find expert help for labeling your datasets. There are third-party data labeling companies with their staff that can label the data for you. This is the best method if you don’t have the right expertise to develop and maintain a datasets.
There are dedicated machine learning algorithms for data labeling. Well, this is the best labeling method if you are dealing with a large amount of data. Also, ML-based data labelling has its quality audits to check if the results are as needed.
Quality Assurance of labelled data
There are quality audits at different levels while developing an AI. Since we are talking about data labeling for AI, the quality of the structured data is pivotal. But why? Because poorly labelled data can affect the performance of your prediction model.
Quality assurance keeps a tab on the consistency of your data labellers. That is, quality assurance in data labelling sees to that if the accuracy of your entire dataset stays the same. After all, labellers, be it a machine or a human, can make errors.
Elements that affect Quality in Data Labelling
A labeller can follow good labelling practice only if they have sufficient knowledge. Also, they have to be aware of the context of the work. Once they understand why they are labelling the given data, they can do better in labelling as well.
Data labelling is a repetitive job. So, keep your employees motivated. Their mood can drastically impact the labelling process. And also, data labelling is an iterative process. Your labellers should be flexible to work with newer datasets as they get updated.
Besides, keep the communication between you and your labelling team active. Pass on any updates on the requirements of a project. Irregular communication can have adverse effects on the process.
Purpose of Labelled data in AI
AI runs over a big database. This data is fed by its developers on their training stage. Data labeling happens prior to this. Let’s say that an AI for autonomous driving is to be designed. The raw data for this model would be a lot of pictures from camera sensors, the output from Lidar sensors, and data from IR sensors. So, this is where you start data labeling.
After collecting all the available data, data labelers label them into different classifications and tag them accordingly. So, a photo will be tagged for pedestrians, road signs, cyclists, other cars, lanes, and any other relevant data. And while training the AI, it studies the labelled data to understand what it is made for. It then uses this data to find suitable objects when it is deployed.
However, the most significant element of labelled data in AI is its ability to update the dataset. As newer unstructured data comes in, the AI automatically tags it and uses it as a dataset. This is a never-ending cycle unless you switch it off. So, an AI model gets better and better over its lifetime. You don’t need to change a single code, even though better codes do help.
Challenges in Data Labeling
Data labeling is a rather new concept, and there are so many concerns in the field. However, this should not stop you from exploiting its possibilities.
With larger data flow, the need for a larger workforce arises. Forget the expense; think about the training for this new workforce. And, how can you efficiently manage such a workforce without any hassle? This is one of the most imminent challenges in data labelling.
It was easy for us to write about Quality Assurance. But, when done, it is a difficult task. Ground truth is the reference outcome. So, if you are making an algorithm for face detection, the algorithm should analyze features in any visual feed and find features that are similar to a face. So, by definition, a human-face is the ground truth for a face detection algorithm. Your labellers should have the capability to bring out consistent results and stay with the ground truth as well. And when the workforce is large, it could become a mess. However, opting for ML-based labelling can work great here.
The data labelling process is subjective and objective. But how? The labelling process solely depends on how the labeller tags the data. It is important to keep quality assurance between the labellers to ensure consistency on the labelled data. In addition, the objective nature of the job compels the developers to draw a silver lining on defining the right and wrong of the project. Thus, it is important that there are frequent auditing feedbacks to keep the process in control.
I know that I asked you to forget about it. But, this is an obvious drawback. More the workforce, the more mouths you have to feed. And there goes the money for training them as well.
However, nothing should stop you from labeling your data. Maybe you don’t have the expertise. So what? Bring in an expert so that you can sit back and relax. Let them do the work for you. Since the industry is cutting edge, this is the best time to start investing.