Synthetic Data Generation

Synthetic data is a common way to help test and train machine learning models. It’s usually easier to use synthetic data for simple applications like data augmentation, but for more complex uses, like explainable AI, it can be challenging.

Single subject tables are the easiest type of table to synthesize. This is because each row describes a unique real-world entity – a customer, a patient, or a company employee – and doesn’t depend on the contents of other rows.

Generative Modeling

The goal of synthetic data generation is to create data that is statistically similar to real-world data. Data teams start with a real-world dataset, learn patterns and correlations, and then use those to create synthetic data that looks, feels, and means the same.

Tabular data, like spreadsheets, text data from chatbots or machine translation software, and unstructured image, video, and audio data are common examples of synthetic data. This type of data can help eliminate the need for regulated or privacy-sensitive information, and it can be produced much faster and cheaper than real data.

Using rule-based or stochastic processes, this generative modeling creates synthetic data that matches the desired structure but doesn’t provide any actual information. This kind of data is commonly used in computer vision training, for example, where the model needs to see a variety of different scenarios and conditions. It can also be generated for stress testing systems to ensure that they can perform under heavy loads.

Randomization

When you create synthetic data, you generate a new set of numbers that mathematically or statistically mirrors its real-world counterpart without revealing the original information. This data is then used in various ways, including for machine learning models. It’s also used in simulations for a variety of industries, from smart stores and robots to automobiles and space exploration.

MOSTLY AI’s patented synthetic data generation platform uses randomization to create the most realistic, high-quality test data for your use cases. Our platform recognizes numeric, categorical, and datetime columns in your input data and automatically translates them into synthetic data. We even eliminate N/A values from your synthetic data while keeping variable statistics like mean, variance, and quantiles intact.

The best part about creating synthetic data is that you don’t have to worry about PII or privacy regulations. Our streamlined process for self-service provisioning makes it easy to design test data for functional and non-functional testing or use in a training model.

Variational Autoencoders

The use of synthetic data has become commonplace in many industries. It is especially important in sectors where real-world data may be sensitive or inaccessible due to privacy concerns or time and financial constraints.

For example, banks and finance firms collect a wealth of customer information, including personally identifiable information (PII). Synthetic data can allow these organizations to train models on this proprietary data without violating privacy.

Moreover, the quality of synthetic data is critical to ensure that the model trained on it performs well. This can be a challenge as it requires multiple manual steps to create the data set, and any assumptions made during these processes can have a large impact on how the data is used. This is why it is imperative to evaluate the sensitivity of features in the latent space of a VAE model before using them for synthetic data generation. Feature importance calculated by sensitivity analysis provides an explanatory framework to understand how the features in the inputted tabular data will impact the way a VAE synthesizes data.

Faker

Lee Sang-hook, better known by his professional name Faker, is one of the most famous League of Legends players in the world. The midline superstar is considered by many to be the best player of all time and has been the cornerstone of SK Telecom T1’s success in both the LCK and on the global stage at MSI and Worlds.

Synthetic data is essential for a variety of use cases, including testing user interfaces and performance. Towards Data Science lists several resources for creating synthetic data, including services that provide ready-made datasets and software libraries such as Python Faker.

Aside from being a legendary League of Legends player, Faker is also an avid social media celebrity and TV personality. The esports star has appeared on shows such as Hello Counselor, where he was joined by members of the K-pop group Red Velvet. The king of the Rift is also a big fan of food and recently tried broccoli for the first time.