Accelerating Model Training with Synthetic Data
F5’s AI Data Fabric is helping us to accelerate the training and deployment of machine learning (ML) models for a variety of use cases. One of the key challenges the AI Data Fabric helps to solve is with respect to scarcity of good training data. With any ML initiative, the quality, diversity, and volume of data are critical to building effective models.
Real-world data has always been the go-to resource for training ML algorithms. The AI Data Fabric certainly benefits from the technology footprint of F5’s extensive customer base and access to high-caliber, real-world data. After all, F5 sits in the data path of nearly half of the world’s applications, with 550 petabytes flowing through F5 products every day.
However, in the past few years, synthetic data has emerged as a compelling source of training data and is rapidly growing in importance to our ML ecosystem.
What is synthetic data?
Synthetic data refers to artificially generated data that mimics the characteristics of real-world datasets. After learning the statistical properties and structures of real data, we can generate artificial data that has the same properties as the authentic data. Using these techniques, the AI Data Fabric can generate massive amounts of data resembling what we collect from customers.
Why use synthetic data?
There are numerous benefits to using synthetic data. First, there’s privacy and compliance. Synthetic data can be produced without sensitive information, making it an excellent choice for our customers who are bound by stringent privacy regulations or security policies. By using synthetic versions of sensitive datasets, we can share and analyze data without putting customer data at risk. We can also be sure that models aren’t trained with customer data.
Second, working with real-world data can be time-consuming and expensive—collecting and labeling massive amounts of data is a real burden, which limits innovation velocity. Generating data significantly reduces costs and accelerates our model development lifecycle.
Real-world data can also be constrained by availability. Good training data is scarce, especially for rare events. Synthetic data helps fill gaps and balances underrepresented classes for specific scenarios. For example, in a dataset for detection of attacks, routine transactions might vastly outnumber malicious ones. With synthetic data, we can overcome this scarcity—our teams can test edge cases that aren’t represented in real-world data, and more easily explore hypothetical situations.
Finally, there’s security. With synthetic data, we can generate adversarial examples that are then used to test model security against attack. Synthetic data even helps to guard against attacks like data poisoning, where attackers manipulate training data to corrupt AI models.
The downsides of synthetic data
While there are many benefits of synthetic data, there are some cautions to be aware of. For example, generating synthetic data requires advanced algorithms and high levels of expertise to make it work. Synthetic data also has challenges around realism—models trained exclusively with synthetic data may not perform well in real-world situations. Either the training data may be overly simplistic, lacking the complexities and nuances of real data, or the models overfit to patterns in synthetic data that might not be present in real scenarios.
Despite these cautions, synthetic data can be very useful in scenarios where real data is scarce, expensive, or sensitive. If we understand its limitations and account for them in the model development process, synthetic data generation is a powerful tool in F5’s machine learning arsenal. Synthetic data helps us go faster and deliver much better outcomes for our customers in the form of reliable ML models.