The field of Data Science and Machine Learning is growing every single day. As new models and algorithms are being proposed with time, these new algorithms and models need enormous data for training and testing. Deep Learning models are gaining so much popularity nowadays, and those models are also data-hungry. Obtaining such a massive amount of data in the context of the different problem statements is quite a hideous, time-consuming, and expensive process. The data is gathered from real-life scenarios, which raises security liabilities and privacy concerns. Most of the data is private and protected by privacy laws and regulations, which hinders the sharing and movement of data between organizations or sometimes between different departments of a single organization—resulting in delaying experiments and testing of products. So the question arises how can this issue be solved? How can the data be made more accessible and open without raising concerns about someone’s privacy?
The solution to this problem is something known as Synthetic data.
So, What is Synthetic Data?
By definition, synthetic data is generated artificially or algorithmically and closely resembles actual data’s underlying structure and property. If the synthesized data is good, it is indistinguishable from real data.
How Many Different Types of Synthetic Data can there be?
The answer to this question is very open-ended, as data can take many forms, but majorly we have
Text data
Audio or Visual data (for example, Images, videos, and audio)
Tabular data
Use cases of synthetic data for machine learning
We will only discuss the use cases of only three types of synthetic data, as mentioned above.
Use of synthetic text data for training NLP models
Synthetic data has applications in the field of natural language processing. For instance, the Alexa AI team at Amazon uses synthetic data to finish the training set for their NLU system (natural language understanding). It provides them with a solid basis for training new languages without existing or enough consumer interaction data.
Using synthetic data for training vision algorithms
Let’s discuss a widespread use case here. Suppose we want to develop an algorithm to detect or count the number of faces in an image. We can use a GAN or some other generative network to generate realistic human faces, i.e., faces that do not exist in the real world, to train the model. Another advantage is that we can generate as much data as we want from these algorithms without breaching anyone’s privacy. But we cannot use real data as it contains some individuals’ faces, so some privacy policies restrict using that data.
Another use case is doing reinforcement learning in a simulated environment. Suppose we want to test a robotic arm designed to grab an object and place it in a box. A reinforcement learning algorithm is designed for this purpose. We need to do experiments to test it because this is how the reinforcement learning algorithm learns. Setting up an experiment in a real-life scenario is quite expensive and time-consuming, limiting the number of different experiments we can perform. But if we do the experiments in the simulated environment, then setting up the experiment is relatively inexpensive as it will not require a robotic arm prototype.
Uses of Tabular data
Tabular synthetic data is artificially generated data that mimics real-world data stored in tables. This data is structured in rows and columns. These tables can contain any data, like a music playlist. For each song, your music player maintains a bunch of information: its name, the singer, its length, its genre, and so on. It can also be a finance record like bank transactions, stock prices, etc.
Synthetic tabular data related to bank transactions are used to train models and design algorithms to detect fraudulent transactions. Stock price data from the past can be used to train and test models for predicting future prices of stocks.
One of the significant advantages of using synthetic data in machine learning is that the developer has control over the data; he can make changes to the data as per the need to test any idea and experiment with that. Meanwhile, a developer can test the model on synthesized data, and it will give a very clear idea of how the model will perform on real-life data. If a developer wants to try a model and waits for real data, then acquiring data can take weeks or even months. Hence, delaying the development and innovation of technology.
Now we are ready to discuss how synthetic data help to resolve the issues related to data privacy.
Many industries depend on the data generated by their customers for innovation and development, but that data contains Personally Identifiable Information (PII), and privacy laws strictly regulate the processing of such data. For instance, the General Data Protection Regulation (GDPR) forbids uses that weren’t explicitly consented to when the organization collected the data. As synthetic data very closely resemble the underlying structure of real data and, at the same time, ensures that no individual present in the real data can be re-identified from the synthetic data. As a result, the processing and sharing of synthetic data have much fewer regulations, resulting in faster developments and innovations and easy access to data.
Conclusion
Synthetic data has many significant advantages. It gives ML developers control over experiments and increases development speed as the data is now more accessible. It promotes collaboration on a bigger scale since data is freely shareable. Additionally, synthetic data guarantees to protect the privacy of the individuals from the real data.
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The field of Data Science and Machine Learning is growing every single day. As new models and algorithms are being proposed with time, these new algorithms and models need enormous data for training and testing. Deep Learning models are gaining so much popularity nowadays, and those models are also data-hungry. Obtaining such a massive amount
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