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How can I generate synthetic data for my specific needs?

Using synthetic data is an efficient way to train and test machine learning models. It saves time and resources from collecting, organizing and labeling real-world data.

The downside to this approach is that it can reveal sensitive information and privacy concerns. If a field is derived from other fields, it might not be possible to hide that relationship in a private dataset.

Real-world data

It takes a lot of time and resources to collect large volumes of real-world data. Synthetic data offers a solution to these challenges and makes it easier for organizations of all sizes to create the test bed they need for their models.

Using synthetic data also means that companies can share datasets without fear of compromising privacy. This is particularly important for industries like healthcare and automotive, where actual patient data can’t be shared due to privacy concerns.

For example, BMW uses virtual reality and gaming engines to simulate a car factory environment for robots and self-driving cars. The simulated data helps BMW fine-tune how workers and machines work together to build cars efficiently, and it also helps the company train its AI algorithms.

Tabular data

There are many ways to generate synthetic data, from simple fake data to sophisticated generative algorithms. The type of data generated depends on the use case, but common examples include tabular data and unstructured text. Companies can build an in-house generation algorithm, use an open source solution or buy an out-of-the-box tool. Each has advantages and disadvantages. For example, an in-house solution can be expensive. An open source solution can be difficult to implement and may trigger privacy issues. A third-party tool can be easier to learn and offer vendor support.

In some cases, generating synthetic data is the only way to get the necessary information for a particular analysis or model. For example, getting access to credit card data can take 6 months or more, and requesting genomic data for rare diseases can take even longer. Using a no-code synthetic data generator can be a cost-effective and faster alternative. Mostly AI, for instance, offers a high-security and privacy-preserving synthetic data generation solution that meets stringent regulatory standards. It uses atomic transformations and differential privacy to ensure that no PII is exposed in the final data set.

Time series data

Whether you’re looking to simulate “black swan” events or just want to improve the performance of your model with additional data points, creating synthetic datasets is one way to do it. These Python packages enable you to do just that.

They use generative modeling to learn the distributions of training data so that they can create similar distributions for new data points. They also come with a web-based UI for generating datasets that you can then use to build models and make predictions.

They’re used in applications from business intelligence to simulations of everything from atoms to galaxies. They’re especially useful for businesses that need to get access to sensitive data but can’t wait months for approvals to obtain real-world data. This is the case with financial services firms such as Facteus which creates synthetic, privacy-preserving datasets on debit and credit card transactions. Other companies are leveraging GANs to generate medical images or modifying real-world data to augment a machine learning dataset for better accuracy and fairness.

Image and video data

One of the most common challenges in data science is gaining access to real-world data. Depending on the field, it can take months to obtain datasets and even more to get approval for them. This is often a major hurdle to innovation. Synthetic data can reduce the time to experiment and allow businesses to generate privacy-preserving versions of data.

There are a number of tools to create synthetic data. A business can build an in-house solution, use open source software or buy a third-party platform. The decision depends on the business’s goals and requirements. Building an in-house solution is resource-intensive but can be highly customizable. An open-source tool may be easier to implement but can trigger privacy issues. Buying an out-of-the-box solution is easy and provides less customization but can help avoid privacy concerns.

For example, Mostly AI offers a platform that enables users to quickly and cost-effectively generate data sets and test machine learning models without relying on actual customer data. The tool is a great option for companies that need to comply with privacy regulations, such as those governing healthcare data.

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