Complete Handbook on Synthetic Data Generation |
Posted: March 4, 2024 |
Living in the age of AI is all about having blessings. Now, machines can do amazing things for us without the intervention of any human. With the help of AI, we can make smart decisions, automate tasks, improve health, and more. But have you ever thought about how AI tools learn to do these things? They need lots of data to be trained. And sometimes, real data is not enough, not available, or not safe to use. In this aspect, synthetic data generation can be truly beneficial. New term to you? Well, synthetic data is data that is created by computers, not by humans. This artificial data mimics the statistical properties of real data but does not reveal any sensitive information about real individuals or entities. According to Gartner, a market research firm, by 2030, there will be a prevalence of synthetic data in AI models instead of real data. So, what else can this data do, and how is it generated? We’ll discuss all of that in this article to help you get deep learning with synthetic data. Let’s get started! What is synthetic data generation?Well, it is a way to create data that looks like real data but is not. It uses algorithms, models, and techniques to make data that has the same features and patterns as real data. However, it does not have any actual data from real datasets. Hope so, you have a clear understanding of the meaning of synthetic data – artificially generated data that reflects the characteristics of real-world data. This method is good for privacy and security, too. This means you can test software and do research without exposing sensitive or personal information. In short, this method acts as a useful and powerful tool for many purposes. Synthetic data has two types: - Structured that contains tabular data - Unstructured that contains image and video data What are the techniques of synthetic data generation?There are 4 techniques to generate synthetic data. These are: 1. Generative AIGenerative AI uses ML models. The model’s algorithms learn patterns and features from the existing data. Then, they create a new synthetic dataset that looks like the original data but is not exactly the same. Some examples of generative AI models are GPT, GANs, and VAEs. · GPTIt is a language model that is trained on tabular data. This model can generate realistic synthetic tabular data. GPT depends on training data to understand the queries. This model uses a large neural network called a transformer to learn from a huge amount of text data. · GANsThese models use two neural networks that compete with each other: a generator and a discriminator. The generator creates the real synthetic data, and the discriminator separates the real data from the synthetic data. In training, the generator fools the model, which results in a new synthetic dataset. · VAEsVAEs use two neural networks that work together: an encoder and a decoder. The encoder compresses the input data into a latent space. The decoder rebuilds the input data from the latent space. In the context of tabular data, VAEs create fake rows of information that mimic the features of the real data exactly. 2. A rules engineThis technique synthesizes the data as per user requests. You tell the engine what rules to follow, and it creates data that follows them. In this context, an example of synthetic data could be setting a rule that names must start with a vowel or that ages should be between 18 and 65. The engine can also keep the data consistent by using the relationships between the data elements. This way, the synthetic data looks like real data, but it is not. A rules engine is good for simple use cases with low complexity. 3. Entity cloningEntity cloning works by copying some parts of the real data and changing other parts slightly. For example, you can clone a person's name by changing one letter or swapping the first and last names. This way, you can create new names that are similar to the original ones but not exactly the same. Entity cloning can help you protect the privacy of the real data while still keeping some of its features and patterns. 4. Data maskingData masking is a way to hide real data with fake data. It helps to protect sensitive data from people who should not see it. For example, you can use data masking to change names, addresses, or credit card numbers in a database. Data masking can be done in different ways, such as replacing, shuffling, or encrypting data. So, this technique is useful for testing, training, or sharing data without risking data security. What are the use cases of synthetic data?There are 2 main synthetic data use cases: A. Software testingGenerating synthetic data plays a crucial role in software testing. Synthetic data creates new representative datasets to check how your software works, how fast it runs, and how reliable it is. Plus, this data has many benefits. For example, you can:
Synthetic data is easier to use and more flexible. Moreover, testing and DevOps teams can gain huge benefits from this data, especially when there is no relevant real-time data available. It is important to consider the biases, quality, and balance of the datasets. In the context of software testing, synthetic data can be used for different purposes, such as:
B. Machine Learning (ML) model trainingThe other use case is machine learning (ML) model training. Why is that? Because synthetic data is faster and easier to get than real data. Also, it helps the model practice in a safe environment before it goes live. This way, the model can learn the patterns from synthetic data and improve its performance. Here are some benefits of synthetic data that attract data scientists to choose synthetic over production data: · AugmentationSynthetic data can be augmented to include more features and scenarios that may not be present in the real data. This can help data scientists test their models and algorithms more thoroughly and robustly. · DiversityThis data can be diverse and representative of different populations and groups that may be underrepresented or missing in the production data. This can help data scientists avoid bias and ensure fairness and accuracy in their results. · ImbalanceNot to mention, synthetic data can be balanced to avoid the problem of imbalance in the production data. Imbalance means that some classes or outcomes are more frequent than others, which can affect the performance and evaluation of the models and algorithms. Synthetic data can be generated to have an equal or proportional distribution of the classes or outcomes. · PrivacyIn addition, production data often contains sensitive information about real people, such as names, addresses, or credit card numbers. Using this data for testing can expose it to unauthorized access or misuse. Synthetic data, on the other hand, does not have any personal information. It is safe and secure to use for testing without violating any privacy laws or ethical standards. · ScarcityProduction data may not be enough or suitable for testing certain scenarios or features. But synthetic data allows data scientists to create any type of data they need, with any size, distribution, or complexity. This way, data scientists overcome the limitations of production data and test their systems more effectively. What to look for when selecting tools to generate synthetic data?You can find a lot of synthetic data generation tools in the market. But choose the one that can:
What is the future of synthetic data generation?With time, synthetic data processes are evolving at a fast rate. Here are some areas that will help businesses make better-informed decisions with synthetic data use cases: 1) Synthetic data operationsThis data can help with data operations, such as data cleaning, data augmentation, and data privacy. Data teams are finding new methods to manage and automate the complete synthetic data lifecycle. This will reduce the time and cost of data preparation. 2) Improved data quality, accuracy, and reliabilityData professionals only consider reliable data for their tasks. For that, synthetic data companies will optimize their synthetic data algorithms to give unbiased and high-quality data. This will create more diverse and realistic data sets and also reduce the bias and noise in the data. 3) Ethical and legal perspectivesWith the increasing ratio of synthetic data, legislators and regulators are also considering its ethical and legal implications. So, business and IT teams should pay attention to these issues when developing. 4) Integration with production dataAdditionally, synthetic data can integrate with production data, such as real-time data, streaming data, and historical data. With that integration, data teams are optimistic about enhancing the value and utility of production data. Also, this data can enable new applications and insights. 5) Emerging use casesThe usage of synthetic data is increasing every day, and so are its applications in artificial intelligence, machine learning, and deep learning. The researchers are seeking ways to improve the development and testing of new models and algorithms. This will also improve the performance and accuracy of existing ones. ConclusionIn conclusion, synthetic data generation is a popular way to create artificial data that looks like real data. There are four techniques to generate this data such as generative AI, rules engine, entity cloning, and data masking. Primarily, it can be used for many purposes, such as testing software and training machine learning models. Furthermore, you can see its multiple benefits, such as protecting privacy, reducing bias, and increasing diversity. And? In the future, synthetic data will be more reliable, unbiased, and confidential than real data.
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