Automotive: it is difficult and slow to get real-world data for robots, drones, and self-driving cars.Learn to process sensitive information by taking a data privacy and anonymization course with Python or R. You can also use it for clinical trials and detecting rare diseases. Healthcare: allow us to share medical records internally and externally while maintaining patient confidentiality.The synthetic data is rendered to test systems on rarer anomalies and improve performance. Quality Assurance: maintaining and testing the quality of application or data systems.It is also used to understand customer behaviors using analytics tools. Financial service: synthetics data is generated to mimic rare events such as fraudulent transactions, anomaly detection, and economic recession.It also helps move private data to the cloud and retains data for analytics. Data Sharing: synthetic data enables enterprises to share sensitive data internally and with third parties.In this section, we will learn how companies use synthetics data to build cost-effective, privacy-friendly, high-performance applications. It is costly to acquire real-world data, clean it, label it, and prepare it for testing or training models. Cost: the data collection takes time and resources.Examples: credit fraud detection, car crashes, and cancer data. Rare Cases: we cannot wait for the rare event to occur and collect real-world data. For example: in image classifiers, we use the shearing, shifting, and rotating of images to increase the size of the dataset and improve model accuracy. Model Performance: generated synthetics data can improve model performance.Testing database, UI, and AI applications on synthetics data is more cost-efficient and secure. Testing: application testing on real-world data is expensive.It will help us avoid cyber and black-box attacks where models infer the details of training data. You can replace names, emails, and address with synthetic data. We need synthetic data for user privacy, application testing, improving model performance, representing rare cases, and reducing the cost of operation. Why Do We Need to Generate Synthetic Data? In the final part, we will explore the Python Faker library and use it to create synthetic data for testing and maintaining user privacy. In the first part of the tutorial, we will learn about why we need synthetic data, its applications, and how to generate it. Even if you get the data, it will take time and resources to clean and process it for machine learning tasks. For example, bank fraud, breast cancer, self-driving cars, and malware attack data are rare to find in the real world. It is costly to collect and clean real-world data, and in some cases, it is rare. But why are we seeing an upward trend of synthetics data? The typical use of synthetics data in machine learning is self-driving vehicles, security, robotics, fraud protection, and healthcare.Īccording to data from Gartner, by 2024, 60% of data used to develop machine learning and analytical applications will be synthetically generated. It is also valid for situations where data is scarce and unbalanced. In the case of machine learning, we use synthetic data to improve model performance. Using synthetic data can help companies test new applications and protect user privacy. For example, to protect the Personally Identifiable Information (PII) or Personal Health Information (PHI) of the users, companies have to implement data protection strategies. The primary purpose of synthetics data is to increase the privacy and integrity of systems. Synthetic data is computer-generated data that is similar to real-world data.
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