Using Synthetic Data for Machine Learning & AI in Python

WebinarJuly 2023

80% of AI projects fail, and more don't even start due to privacy constraints. This is where AI-generated synthetic data comes in. It's an anonymization technology seen as the key enabler for artificial intelligence.

Rewatch this training to discover what synthetic data is, how it protects privacy, and how it's being used to accelerate AI adoption in banking, healthcare, and many other industries. You will create a highly representative synthetic dataset yourself, learn how to assess its quality and use it for privacy-preserving machine learning. And as a bonus exercise, we'll look into smart imputation with synthetic data to save you time on data pre-processing!

Key Takeaways:

Learn when synthetic data can be helpful for protecting privacy.
Learn how to create synthetic datasets.
Learn how to assess the quality of synthetic datasets.

Code along with Alexandra on DataCamp Workspace

Generate synthetic data using MOSTLY AI - Use the ‘AI/ML training’ set

Summary

Synthetic data is increasingly important for artificial intelligence, especially for maintaining data privacy in sectors such as finance and healthcare. Alexandra Ebert, Chief Trust Officer at Mostly AI, presents an in-depth explanation of synthetic data's advantages, emphasizing its potential to substitute sensitive real-world data without endangering privacy or usefulness. This technology solves the data shortage challenge faced by companies due to strict privacy laws like GDPR. Unlike traditional anonymity methods, synthetic data offers a privacy-preserving copy of actual data without the threat of re-identification. Ebert underscores synthetic data's role in accelerating data access, enhancing precision, and promoting cooperation between companies. She also mentions the significance of intelligent imputation to fill in missing values and improve data quality. Throughout the webinar, Ebert demonstrates how synthetic data can be nearly as accurate as real data, making it a potent driver for machine learning and analytics.

Key Takeaways:

Synthetic data removes privacy risks while maintaining data utility.
It significantly accelerates data access and democratizes data usage.
Intelligent imputation with synthetic data improves data quality by filling missing values.
Synthetic data facilitates effective cooperation with external partners by ensuring privacy.
It provides a scalable solution to the data shortage challenge caused by privacy laws.

Deep Dives

The Essential Role of Synthetic Data in AI

Synthetic data is vital in the AI industry, acting as a significant driver for privacy-pre ...
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serving machine learning and analytics. As Alexandra Ebert explains, synthetic data is particularly important for sectors like finance and healthcare, where data privacy is a top priority. With privacy laws such as GDPR and the California Consumer Privacy Act becoming stricter, companies face a data shortage challenge. They have vast amounts of data but can use only a small amount due to compliance issues. Synthetic data provides a solution by creating a non-identifiable copy that maintains the statistical properties of real data, thus removing the risk of re-identification. As Ebert notes, "Synthetic data allows companies to unlock and utilize their data while fully complying with privacy laws."

Benefits Over Traditional Anonymization

Traditional data anonymization methods, like masking and obfuscation, are not sufficient in the era of big data. Ebert mentions that these methods are destructive and not enough for protecting privacy, as they often lead to re-identification risks. In comparison, synthetic data creation uses AI to learn patterns and structures in datasets without retaining any real data points. This results in a synthetic dataset that is statistically similar to the original but free from privacy concerns. Additionally, synthetic data allows companies to retain the full range of their data attributes, enabling more precise machine learning models and analytics. This technological progress is reflected in Gartner's prediction that by next year, 60% of AI training data will be synthetic.

Intelligent Imputation and Data Quality

A key feature of synthetic data is its potential to perform intelligent imputation, filling in missing values to enhance the overall quality and usability of datasets. Ebert shows how this feature can significantly improve data accuracy, especially in datasets with incomplete information. By training a machine learning model on synthetic data, companies can achieve performance that closely resembles models trained on real data. This capability is particularly helpful for datasets with missing demographic or behavioral information, where traditional imputation methods may not be enough. Intelligent imputation ensures that the synthetic data retains its analytical value, making it a reliable substitute for real-world data in machine learning applications.

Applications and Future Prospects

Synthetic data is not only a tool for privacy preservation but also a driver for innovation across different sectors. It enables safe data sharing and cooperation, allowing companies to partner with startups and external vendors without endangering privacy. Ebert highlights its use in responsible AI, supporting AI governance, fairness, and explainability. The flexibility of synthetic data is evident in its application to different data types, including time series and behavioral data. As the technology evolves, synthetic data is set to become a standard practice in data-driven innovation, addressing both privacy concerns and the need for high-quality data in AI development.