The data labeling market is growing at a remarkable rate. Recent estimates suggest that it will be worth over $38 billion by 2028. While it is fascinating to observe the fast pace this market is growing, many laypeople and even some IT professionals are also confused about the concept behind data labeling.
What is data labeling and what are its implications for the data science profession?
What Are the Principles of Data Labeling?
Working with AI (artificial intelligence) models involves various components, one of the most important of them being data labeling. To put it in simple words, data labeling refers to adding tags to data. Another way to understand this concept is to think of it as identifying data. Data labeling is also called data annotation, and it is a core component of the preprocessing stage of a machine learning (ML) model.
Through data labeling, a machine learning model becomes capable of understanding raw data. That means when you feed an image of a banana, the machine can understand that it is a fruit, and more particularly, what fruit it is.
There are a lot of tools that perform data labeling tasks. These include Snorkel AI, SuperAnnotate and Amazon SageMaker. These tools offer tremendous scalability and bolster the efficiency of the data labeling process.
The reasons data scientists need to invest in data labeling cannot be understated. It is very important for any machine learning model to have correctly annotated data. It improves accuracy and efficiency. Accurately annotated data also plays a key role in identifying problems and proposing solutions.
In order to understand the importance of carrying out data labeling properly, we will discuss in detail 5 facts that you should know to understand the process of data labeling better.
Human input plays a key role in data labeling
Any company, business, or individual working with machine learning models needs software and processes that assist the data labeling process. However, what they need more are skilled data annotators who can refine raw data, give them a structure, and label it. This data is known as ‘training data‘, and it is the very foundation of machine learning models. KDNuggets has some more information on this concept.
Through this process, analysts can identify unique variables in the datasets. As a result, they can then select the optimal data predictors. By training the model through data vectors, the model itself becomes capable of identifying and distinguishing raw data.
As you can see, human input in the form of data annotators and analysts plays a key role in making a machine learning model accurate and predictable.
Unlabeled data and labeled data
Data can be both labeled and unlabeled in machine learning models, and both have their use cases. However, labeled data is much more useful when it comes to the real application of the system. At the same time, it is way more expensive and cumbersome to store and maintain labeled data.
Labeled data is also called supervised data, while unlabeled data is called unsupervised data. Unsupervised data is useful when it comes to identifying clusters of new data and helping in categorizing them. Both these components work together to make a machine learning system efficient.
There are many approaches to data labeling
There is no single correct approach to data labeling. Depending on the use case, budget, time, and a host of other factors, developers can choose from any of the several approaches to data labeling.
Internal labeling is the most accurate approach, but it comes with a lot of responsibilities. In internal labeling, in-house data scientists take the task of data labeling upon themselves. As a result, the work is generally very accurate and precise. However, it also has a couple of downsides. First, internal labeling necessitates the need for an in-house team of experts, which can be both difficult and expensive. Apart from that, internal labeling requires a lot of time. Time and money are the two biggest constraints to internal data labeling.
Apart from internal labeling, there are models like external labeling and crowdsourcing. Beyond that, there are approaches like synthetic labeling and programmatic labeling. In the latter approaches, data labeling is done through algorithms and existing datasets. While these approaches deliver much faster results, they lack the accuracy of internal labeling or outsourcing.
Data labeling makes natural language processing (NLP) possible
NLP is an important area of research in modern computer science and artificial intelligence. NLP refers to the ability of a machine to understand language as humans use them in their day-to-day lives. It plays a very important role in the interactions between machines and humans. Data labeling is the first step toward making natural language processing possible.
Phonetic annotation and text annotation are very important for natural language processing. It is the same technology that is revolutionizing how humans interact with self-serving devices. However, natural language processing is not the only use case of data labeling. There are many other areas where it plays a very important role.
The biggest challenges to data labeling are human errors and time
Data labeling comes with a range of challenges and constraints. However, the most relevant among them are human-driven errors and the sheer time that such a project takes. As mentioned earlier, humans play a key role in data labeling. Consequently, the errors that humans make also have a big impact on how accurate the model is. Apart from that, it takes a lot of time to complete the process and make it accurate.
Once we overcome these two primary challenges, the efficiency of data labeling will increase significantly.
Understand the Principles of Data Labeling
Data labeling is an invaluable part of every data scientist’s job. You need to understand the fundamentals and make sure the process is properly executed to make the most of your machine learning strategy. The guidelines listed above should help make sure it is handled properly.
