Today, data is being produced on a massive scale, presenting excellent opportunities for machine learning (ML) initiatives. However, a significant portion of this data remains out-of-bound for data scientists and ML practitioners. Strict privacy regulations, high costs, and long processing times hinder data processing.
As a result, Gartner estimates that 85 percent of AI projects fail. This is where synthetic data proves to be beneficial.
Synthetic data is artificial data that is system-generated with the help of detailed algorithms and simulations. It is completely anonymized data and is an excellent substitute for real data, as it allows organizations to create training data on-demand, in whatever magnitude they desire.
What is synthetic data?
AI algorithms create synthetic data artificially, but it is trained on real datasets and possesses the same properties as original data. Since synthetic data does not have one-to-one correlations to actual data, there are fewer chances of re-identification.
Consequently, data scientists can confidently replicate and use data for testing and modeling purposes without the risk of exposing personally identifiable information (PII) and falling afoul of regulatory agencies.
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How is synthetic data generated?
There are several ways to generate synthetic data. Easier options include Monte Carlo simulations and drawing numbers from a distribution set, but a generative model is usually preferred if the datasets are complex.
Generative models are based on neural networks that automatically learn from the patterns found in real-world data and produce information that accurately matches it. Generative Adversarial Networks (GANs) and Variational Autoencoder (VAEs) are the two common generative model architectures.
In the GAN model, two neural network models (called generator and discriminator) compete in a zero-sum game where one network’s gain is another’s loss. VAEs, on the other hand, are unsupervised models that work on the encoder-decoder concept.
What tools help with synthetic data generation?
Below are examples of tools you can use to create synthetic data.
- Datagen is a synthetic dataset solution that delivers photorealistic datasets that find use in the Internet of Things (IoT), robotics, and augmented reality (AR).
- Built upon Matplotlib, NumPy, and SciPy, Scikit-learn is an open-source Python library that provides tools to generate synthetic data sets.
- Pydgben is a Python library that creates common entries like name, job, credit card numbers, email addresses, etc.
- Parallel Domain is a synthetic data platform that generates high-quality sensor data to improve ML models and computer vision workflows.
Benefits of using synthetic data
When it comes to building machine learning models, synthetic data is more scalable, easier to use, and more cost-effective than other types of data.
ML models consume massive amounts of data. It is simply impossible to obtain such large volumes of relevant data for training and testing purposes. With synthetic data tools, data scientists can create as many copies of data as they want or need to build high-quality AI/ML models.
Ease of use
When working with real-world data, it’s critical to protect personal information, remove inaccuracies, and handle data efficiently in varying formats. Synthetic data is far easier to work with as it masks private information, eliminates errors, and standardizes formats for more straightforward labeling.
Acquiring real training data can cost businesses a lot of money. In addition, manually labeling them is time-consuming. With synthetic data generation tools, the process is simplified and turns out to be a more cost-effective and quicker process.
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Challenges of using synthetic data
Synthetic data offers several benefits, yet it has certain limitations. For example, one of the significant drawbacks is that effective synthetic data use requires highly skilled analysts in who know how to work with sophisticated data generator tools. This is often difficult as there is a shortage of qualified AI workers in the job market.
Further, synthetic data is only as good as the original data, and real data is often riddled with biases. So, when neural networks are trained on biased historical data, they reflect the same biases. This can often lead to inaccuracies in the output of machine learning models.
Use cases of synthetic data
Two of the most prominent use cases for synthetic data are autonomous vehicles and healthcare.
Autonomous vehicles are by far the best use case example of synthetic data. Automotive manufacturers must consider millions of scenarios and collect data accordingly to build safe vehicles.
It is impossible to do this in reality, but with synthetic data, organizations can produce millions or even billions of permutations of any imaginable driving scenario to arrive at a safe driving solution.
Healthcare is a highly regulated industry with strict laws governing patient data usage. Since synthetic data is entirely anonymous and poses no risk of re-identification, medical organizations can easily use it for conducting scientific research, clinical trials, and training ML models in the healthcare industry.
The future of synthetic data
Synthetic data generation is a revolutionary way to create cost-effective and highly scalable data. As awareness about synthetic data and its various benefits grows, more and more businesses will tap into its potential to reap benefits.
Moreover, with tightening privacy laws, organizations will have no choice but to turn to synthetic data. Therefore, it will continue to rise in popularity until it is completely mainstream.
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