Synthetic Data Generation

Synthetic data has become vital in AI and machine learning, especially when engineers face challenges such as class imbalance, limited data access, privacy concerns, or the need to simulate rare edge cases. FAANG companies and AI-first organizations rely on synthetic data to overcome these hurdles, and they expect the candidates to understand it in theory and practice. Interviewers use questions about synthetic data to assess whether you can build models and reason about their data foundations.

A typical interview prompt might be: “How would you generate synthetic data for a machine learning project, particularly in tabular form? What are the trade-offs between methods like SMOTE and CTGAN, and how would you evaluate the effectiveness and safety of your generated data?” You must demonstrate fluency with tools, judgment, and awareness of the evaluation challenges and risks to answer well.

Why is synthetic data important?

Synthetic data addresses real-world constraints that make traditional data collection impractical or even impossible. Perfect datasets are rare in production environments, and synthetic data fills the gap between what we have and need.

Let’s now look at four real-world challenges where synthetic data plays a critical role:

• Handling class imbalance in critical applications: In domains like fraud detection or credit scoring, the minority class is extremely rare but crucial to model well. Synthetic data techniques like SMOTE help generate realistic new samples for the underrepresented class, improving recall and decision boundary coverage.

• Protecting privacy in regulated domains: Data sharing is restricted in healthcare, finance, and legal systems due to privacy laws like HIPAA and GDPR. Synthetic data enables teams to simulate realistic datasets without exposing any real user information, supporting compliance and safe collaboration.

• Simulating rare or dangerous scenarios: Collecting real data for edge cases like crashes or failures in robotics and autonomous systems is often unsafe or infeasible. Synthetic data allows engineers to simulate these conditions virtually, ensuring that models learn from rarely encountered situations.

• Accelerating model development and iteration: Waiting for real data can slow development. Synthetic data can be generated on demand, enabling engineers to rapidly prototype, validate, and refine models without delays from data collection or labeling.

Now that we’ve explored why synthetic data matters and where it can make the biggest impact, let’s turn to how it is created. Several practical methods depend on your goal, data type, and complexity needs. We’ll start with the simplest, i.e., random data generation using basic statistical distribution, and then build up to more advanced techniques like SMOTE and CTGAN.

How to generate random data with NumPy

The simplest way to generate synthetic data is to sample from statistical distributions. Suppose you’re building an ...