Synthetic data is a powerful tool for creating datasets. While it may not be as accurate as real data, it can help you test and develop your machine-learning models in a safe and controlled environment.
Many companies struggle to acquire enough real-world data for their AI models in a timely fashion. Moreover, collecting and hand-labeling data is costly and time-consuming.
It’s a safe way to train machine-learning models
Data synthesis can be a useful tool for many use cases. For example, it can be used to generate simulated data for software testing or to create test data for a model that will be deployed in a real-world environment. Creating this type of synthetic data is much faster and more cost-effective than collecting the corresponding real data.
Synthetic data also allows you to train your models without risking privacy regulations. This is important because privacy violations can lead to expensive lawsuits and damage to a company’s reputation.
In addition, synthetic data can be used to explore edge cases that may not be present in real-world data sets. For example, a self-driving car can be trained on fake road accidents to improve its ability to respond to dangerous situations. This type of augmentation can be especially useful for training models that handle unstructured data such as text or natural language. It is also an excellent option for companies that have a lot of privacy restrictions and need to test their products.
It’s a cost-effective way to train machine-learning models
While real data will always be preferable for most machine learning use cases, there are times when it is simply unavailable. This may be due to regulatory compliance, data security concerns, or lack of resources. In these situations, synthetic data is a good option.
There are a variety of Python-based libraries available to generate synthetic data for various business needs. These include tools for generating images, text, and video. Some are open source, while others offer more robust and quality-controlled options. Partnering with companies that specialize in this field obviates the need for businesses to invest in IT resources and can reduce costs.
Synthetic data is increasingly being used in several different industries. For example, it can be used to create training datasets for robotics and automotive systems. It can also be used to train machine learning models for security and fraud detection without compromising privacy. Healthcare and life sciences are another major area for using this type of data.
It’s a way to protect patient confidentiality
Synthetic data is a method of generating fake data that mimics the structure and distribution of real data. It can be used to reduce privacy risks and meet regulatory requirements for clinical trials. It also allows clinical researchers to test hypotheses without breaking patient confidentiality laws.
One example of synthetic data is the generation of images to help a neural network identify rare diseases. For instance, a synthetic image can be used to train an algorithm to detect the chromophobe subtype of renal cell carcinoma, which is often missed in clinical trials.
Unlike real-world image data, which must be labeled manually before it can be used to train AI models, synthetic data comes with perfect data labels. This means that artificial data can improve the performance of AI algorithms and accelerate innovation in healthcare. However, there are some limitations to using synthetic data. First, it cannot fully replicate the complexity and diversity of real-world data.
It’s a way to test machine-learning models
The emergence of synthetic data is changing how machine learning models are developed. Increasingly, teams use it to test their models without compromising real raw or de-identified data. This approach is becoming increasingly popular in areas such as health care, finance, and logistics. Companies like Anthem, the largest health insurance company in the US, have large troves of patient medical data that they can use to train AI fraud prevention and personalized patient care models.
The growing popularity of synthetic data generation is also making it possible to conduct research studies in areas where real data may be difficult or impossible to acquire. For example, real car driving data is very expensive and takes a long time to collect. As a result, it’s hard for small upstarts to compete with larger technology giants such as Google (Waymo) in the autonomous vehicle market.
Synthetic data can be a great way to compare models and determine the best one for a specific use case. However, it’s important to understand the importance of data utility. Three core indicators include field correlation stability, deep structure stability, and field distribution stability. These metrics are included in Gretel’s Synthetic Data Quality Score report.
