Data Labeling is an essential process in the field of Machine Learning. It involves associating labels with data, to make it usable byMachine Learning algorithms. "Fed with this 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 (e.g. detecting vehicles on a freeway).

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There are several methods of data labeling, each with its own advantages and disadvantages. Here are some common examples:

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1. Manual Data Labeling‍

This is the most common and simplest method. It consists in using a human to manually label the data. This method is particularly useful for low quality data (fuzzy image sets) that require human interpretation or for complex tasks that require human thinking, understanding or interpretation. However, it can be costly and time consuming, especially when the data is large. It can also require a number of reviews to limit errors of inattention and other natural approximations when a person spends several hours on the same data set.

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2. Automated Data Labeling

This is the fastest and most cost-effective method, but it can be less accurate than manual data labeling, or not accurate at all. It uses learning algorithms to automatically label data. This method is especially useful for higher quality data and for simple tasks that do not require human understanding. However, approximations can be numerous, and especially atypical, especially for low quality images or videos. It is rare that this method is self-sufficient in obtaining quality results - it is very often associated with human quality reviews (corrections made by a team of Data Labelers).

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3. Hybrid Data Labeling

This is a combination of the two previous methods. It involves using a human to label some data, while others are labeled automatically. This method can be particularly useful when data is of average quality, and some tasks are complex while others are simple. It can also involve using features of data-labeling platforms, such asActive Learningto continuously improve model results and facilitate the work of data labelers.

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There is no pre-determined solution to accurately label your data. The best approach is to spend a few hours defining 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 a functional specialization or not, ...)
• Expected features of the labeling platform(tracking, ergonomics, annotation types, possible activation of Active Learning functionalities, etc.)

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It is important to choose the right data labelling method: the best method is the one that is adapted to your stakes, your quality requirements, your means and the nature of the tasks to be accomplished. Remember that poor quality data labelling can lead to inaccurate and useless results!

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Despite advances in recent years, data labeling remains a tedious and expensive task for many machine learning professionals. However, it remains essential for training and improving machine learning algorithms, and new solutions are constantly being developed. Remember that a good AI product does not only rely on models: to build your products, you will need massive and quality data!