3 Data Labeling methods for your AI models
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Data Labeling is a essential process in the field of machine learning. It consists in associating labels or labels to data, in order to make them usable by machine learning algorithms (Machine Learning or Deep Learning). “Powered” by these processed and enriched data, an AI prediction model can learn to perform a specific task, such as recognizing speech in a defined language or detecting objects in an image (example: detecting vehicles on a highway).
There are several Data Labeling methods, each with its own pros and cons. Some common examples include:
1. Manual Data Labeling
This is the most common and easiest method. It consists in using a human to label data manually. This method is particularly useful for low-quality data (a set of fuzzy images that require human interpretation) or for complex tasks that require. human reflection, understanding, or interpretation. However, it can be expensive and time consuming, especially when the data is big. It may also require a number of reviews to limit careless errors and other natural approximations when a person spends several hours on the same data set.
2. Automated Data Labeling
This is the fastest and most economical method, but it may be less accurate than manual data labeling, or not accurate at all. It uses learning algorithms to label data automatically. This method is especially useful for high quality data and for simple tasks that don't require human understanding. However, the approximations can be numerous, and especially atypical, especially for images or videos of low quality. It is rare for this method to be self-sufficient in order to obtain quality results. - it is very often associated with human quality reviews (corrections made by a team of Data Labelers).
3. Hybrid Data Labeling
It is a combination of the two previous methods. It consists of use a human to label some data, while others are automatically labelled. This method can be especially useful when the data is of average quality and some tasks are complex while others are simple. It can also include using features from Data Labeling platforms, such as the Active Learning, in order to continuously improve the results of the model and facilitate the work of Data Labelers.
There is no pre-determined solution to label your data accurately. The best approach is to set aside a few hours to define a labelling strategy. Here is a list of criteria that can be determined in advance of any annotation project:
- Number of Data Labelers required
- Sourcing format (internal, external, profiles with or without functional specialization, etc.)
- Expected functionalities of the labeling platform (Tracking, ergonomics, types of annotation, possible activation of Active Learning functionalities,...)
💡 It is important to choose the right Data Labeling method: the best method is the one that is adapted to your challenges, to your quality requirements, your resources as well as the nature of the tasks to be performed. Remember that labeling poor quality data can lead to inaccurate and useless results!
Despite the progress made in recent years, Data Labeling remains a tedious and expensive task for many professionals in the field of Machine Learning. However, it remains essential for training and improving machine learning algorithms, and new solutions are constantly being developed. Remember that a good AI product isn't just about models: to build your products, you will need massive and quality data!
