One of the key challenges of AI and ML development is related to their significant dependency on data. The more diversified, accurate, and reliable sources you use for model training, the better outcomes and the more efficient performance of your solutions you can expect to get. However, the use of data is always associated with privacy and compliance concerns. Moreover, many domains lack high-quality data that can be used. Does it mean that the introduction of AI-powered solutions is not possible in such fields? Definitely not, if developers rely on synthetic data generation.
In this article, we are going to talk exactly about this approach to ML model training. We will explore the role and value of synthetic data, explain the existing approach to its creation, and mention its real-life applications.
Synthetic Data: What Is It?
Synthetic data is artificially generated information. It mimics the statistical properties, structure, and relationships found in real-world data. This data is not collected from real events or users. Instead, it is created through simulations, generative models, or rule-based systems.
Such data is designed to be as close as possible to actual datasets and helps avoid the privacy, legal, and ethical issues that are often typical for the use of real data.
Synthetic data is especially beneficial for machine learning. In this domain, it is used to train, test, and validate models under safe and controlled conditions without the need to rely on sensitive or difficult-to-obtain information.
Examples of Synthetic Data for Machine Learning
The types of artificially generated data include different formats.
- Text. Machine learning training data may include simulated customer reviews, chatbot interactions, legal contracts, and any other textual information.
- Images. For instance, to minimize privacy concerns, developers can rely on AI-generated faces for facial recognition training or road scenes for autonomous vehicle testing.
- Medical records. Healthcare is one of the most strictly regulated industries today. Instead of applying real patient diagnosis histories, X-rays, or lab results, development teams can use artificially generated data that match real patient patterns but are not traceable to any individual.
- Financial data. Artificially generated datasets representing bank transactions or financial records can be helpful for fraud detection or risk modeling.
Types of Synthetic Data
There are several categories of synthetic data that can be used for machine learning training today.
Fully Synthetic Data
Such datasets are generated from scratch. No original real-world data is used in the final output. Synthetic data of this type is an excellent option to train models developed for highly regulated environments where privacy is critical.
Partially Synthetic Data
In these datasets, only sensitive or missing attributes are replaced with simulated values. The rest of the data remains real. Such data is applied when partial real-world context is still required.
Hybrid Data
This is a combination of real and synthetic data used to augment datasets, balance class distributions, or fill in gaps (for instance, for rare-event scenarios or underrepresented groups).
Synthetic Data Generation: Why Does It Matter?
It is predicted that by 2030, synthetic data will become the primary source of training data for AI systems. Of course, we can’t expect that it will entirely replace real-world data. But it will seriously minimize its use, where it is limited by practical and regulatory constraints. This shift is attributed to the flexibility and privacy advantages of synthetic data generation.
In many sectors and regions, collecting large, labeled datasets is slow, expensive, or even practically impossible. Synthetic data can help overcome these difficulties.
Apart from this, its use can efficiently address privacy and fairness-related issues. Synthetic data machine learning training helps developers avoid the risks of compromising personal data, as artificially generated datasets are not tied to any real individual. Moreover, this approach to obtaining data can solve issues such as class imbalance or underrepresentation of particular social groups. As a result, it becomes easier to build more inclusive models and ensure that minority cases are sufficiently covered.
How to Generate Synthetic Data
Rather than collecting real-world data, developers create it algorithmically. Here are the most widely used methods for generating such data.
Generative Adversarial Networks (GANs)
GANs consist of two neural networks. The first one is a generator that creates fake data. The second one is a discriminator. It evaluates whether the data is real or synthetic.
They work in a feedback loop and constantly improve each other until the synthetic data becomes nearly indistinguishable from real data.
This approach provides high-quality and realistic output, which is highly valuable for image generation, facial synthesis, and text-to-image tasks.
Variational Autoencoders (VAEs)
VAEs encode real data into a latent space. Then, they decode it to generate new samples. This method is more stable than GANs and stands out for better interpretability and controllable generation. Therefore, it is better suited for use cases where the latent structure of data matters (for example, for sensor data or structured tabular formats).
Simulation-Based Methods
In this case, physics engines or behavioral modeling are used to simulate conditions typical for the real world. These models are good for high-risk real-world scenarios. Today, they are widely applied in such domains as healthcare, robotics, and autonomous driving.
Rule-Based Generation
This approach presupposes using predefined rules or logic to generate data. It’s particularly effective in domains with strong regulatory frameworks or structured datasets, such as finance or insurance. The interpretability and transparency of such data generation processes remain very high.
Popular Tools for Generating Machine Learning Training Data
There are various platforms and tools that you can rely on to generate synthetic data. They have their weaknesses and strengths and are designed to serve different goals.
SDV (Synthetic Data Vault)
It is an open-source Python library used to generate synthetic data. SDV leverages probabilistic models and deep learning to accurately mimic complex real-world data structures.
Gretel.ai
It is a cloud-based, API-driven tool that is able to produce synthetic datasets across structured, semi-structured, and unstructured formats. This tool is commonly used in privacy-sensitive sectors.
Mostly AI
Mostly AI is an enterprise-grade platform. It is focused on the realistic simulation of customer behavior using advanced privacy engines. It provides bias-elimination features and offers a no-code UI that is accessible to a wide audience of users.
Unity Perception
This open-source tool enables the creation of annotated synthetic image and video datasets for computer vision tasks.
NVIDIA Omniverse Replicator
This is a powerful real‑time 3D simulation and collaboration platform that allows developers to generate physically accurate 3D synthetic datasets. Created datasets are often used for robotics inspection, defect detection, and autonomous navigation across diverse domains.
Synthetic vs. Real Data
Synthetic data lets businesses create large-scale datasets tailored to specific scenarios. One of its strongest advantages is privacy, as it doesn’t contain real-world personal identifiers. Moreover, it significantly reduces compliance risks with data protection regulations. It is also easier to label, making it cost-effective for training models.
At the same time, data of this type can lack the nuanced complexity of real-world behavior. If not carefully validated, it may also include hidden biases or contain patterns that aren’t truly representative.
Real data, by contrast, can offer a rich and authentic view of actual human behavior and business operations. It adds realism and unpredictability, but it also comes with serious challenges. Real data can be expensive to collect, time-consuming to label, and limited in scope. Apart from this, its application can be legally restricted due to privacy laws.
In the table below, you can find a brief comparison of these two data types.
Hybrid Approach
Each type of data has its strengths and limitations. Neither real nor synthetic data can fully cover the needs of organizations that need to train their ML models. That’s why a lot of development teams prefer a hybrid strategy.
They combine real data (to ensure realism) with synthetic data (to achieve enhanced scale, privacy, and scenario coverage).
Quality Metrics
How can you understand that your data can be used for ML model training? There are some aspects to analyze before the ML model deployment.
- Fidelity. You should evaluate how closely synthetic data mirrors real data distributions.
- Utility. It’s vital to test how well models trained on synthetic data perform on real-world tasks.
- Privacy. You need to estimate the risk of re-identification or sensitive information leakage.
Real-Life Use Cases of Synthetic Data
To better understand the significance of synthetic data generation in AI development today, it will be helpful to consider some real-life applications of this type of data.
Autonomous Driving
Artificially generated data can be used to simulate rare or dangerous driving scenarios like heavy rain or emergency maneuvers.
For example, Waymo’s software tool, known as SimulationCity, relies on synthetic journeys based on real-world data to safely test edge cases.
Healthcare
Synthetic data plays a crucial role in modeling patient records with rare diseases without risking privacy.
Synthea is an excellent example of an open-source tool for creating such type of data. It is positioned as a synthetic patient generator that can provide a full medical history of realistic (but not real) patients.
Finance
In the finance industry, synthetic data is commonly used for stress-testing fraud detection systems and generating realistic fraudulent transaction patterns. Apart from this, such data can also be used for other purposes.
For instance, JPMorgan relies on synthetic datasets that cover AML patterns, customer journeys, payment transactions, and market execution data.
Retail
Major retailers like Walmart and Amazon use synthetic personas to replicate consumer preferences, fill data gaps due to privacy restrictions, and reduce commercial bias. This improves recommendation systems and marketing strategies.
For example, RetailSynth is one of the available simulation environments that can be utilized to create synthetic retail datasets.
Benefits of Using Synthetic Data for ML Models
Artificial generation of realistic datasets enables businesses to accelerate model development, reduce risks, and boost innovation even in the most highly regulated sectors. Let’s take a closer look at the most important business benefits that make synthetic data a strategic asset in modern ML training.
Reduced Costs
Such processes as collecting, cleaning, and labeling real-world data can require significant investments. Synthetic data generation helps significantly reduce these costs. It is possible thanks to producing large, high-quality datasets without the need for manual data collection or annotation.
Data Privacy and Compliance
The use of synthetic datasets eliminates reliance on personally identifiable information. As a result, these datasets are perfect for such highly regulated industries as healthcare, finance, and insurance. Thanks to synthetic data machine learning training, organizations can work with their models without violating GDPR, HIPAA, or any other data protection regulations.
Better ML Model Performance
Synthetic data can introduce balanced samples, include rare events, and cover edge cases. All this is crucial for filling gaps in real datasets. This helps organizations train more accurate, robust, and generalizable ML models, especially in safety-critical use cases like fraud detection and autonomous vehicles.
Increased Flexibility and Control
Businesses have full control over the distribution of their synthetic datasets. It means that they can leverage customized scenario simulations, focused efforts to minimize bias, and rigorous model testing under rare or extreme conditions.
Faster Time-to-market
Synthetic data can be generated on demand. Thanks to this, businesses can accelerate the ML model development lifecycle. Without lengthy data acquisition processes, they can move faster from idea to deployment.
Safer Experimentation and Innovation
With this approach to data generation, teams can experiment more easily with new features, algorithms, or architectures without compromising highly sensitive customer data. This ensures a safe sandbox for innovation.
Global Accessibility
In some regions, innovation is slowed due to limited access to real-world data. Synthetic data offers a powerful alternative. It enables companies to simulate local environments, behaviors, or languages, which facilitates faster adaptation and localization of AI solutions.
Wrapping Up
Machine learning is shaping the future of many industries, including healthcare, finance, retail, and many others. That’s why the role of high-quality, diverse, and privacy-safe data is more important than ever before. Synthetic data generation offers a powerful solution. It provides organizations with access to realistic, scalable datasets without the legal concerns that are associated with real-world data. Though it can’t replace real datasets, it can greatly complement them and allow developers to deliver efficient ML solutions designed for different goals.
If you are also planning to launch your AI-powered system or scale an existing one and are looking for a reliable tech partner, at Tensorway, we will always be happy to assist you. Contact us to explore how we can accelerate your machine learning projects.
FAQs
What is the value of synthetic data for machine learning?
The use of synthetic data ensures scalable, diverse, and controlled datasets for training ML models. This type of data helps overcome such issues as data scarcity, privacy concerns, and biased datasets. Thanks to it, developers can accelerate experimentation without relying on sensitive real-world data.
Can synthetic data fully replace real data in machine learning training?
It can replace real-world data in some cases, but not entirely. Synthetic data should be viewed as a complement to, not a replacement for, real data. It enhances data availability and quality. However, real-world validation remains essential to make sure that ML models can perform well in actual deployment scenarios.
What tools can be used for synthetic data generation?
Today, there are many tools that utilize different approaches to creating high-quality datasets. For example, some of them rely on rule-based simulations, while others mainly use statistical modeling. The most well-known names include NVIDIA’s Omniverse, Mostly AI, and Gretel.ai.
Is there any difference between synthetic and augmented data?
Synthetic data is entirely artificial. It is created fully from scratch with the help of simulations and generative models. Augmented data is based on real samples that are transformed via cropping, rotation, or noise addition. Both these types increase available data volume, but synthetic data provides more flexibility.
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