Generative AI Tutorials

Generative AI - Methods for Generating Synthetic Data

Methods for Generating Synthetic Data

Statistical Distribution Methods

Methodology: Using real data, statistical distributions (such as normal and exponential) are identified through analysis. To produce a dataset that is statistically similar to the original, synthetic samples are then produced from these distributions.

Applications: useful for building datasets in which precise data points are not as important as the general statistical features.

Model-Based Methods

Methodology: Regression models and decision trees are examples of machine learning models that are trained on actual data in order to extract its properties. The artificial data produced by these models has the same statistical characteristics as the original data.

Applications: Suitable for creating hybrid datasets to improve model training by combining synthetic and real data.

Deep Learning Methods

Generative Adversarial Networks (GANs): GANs are made up of discriminator and generator neural networks, which collaborate to produce synthetic data that is realistic. While the discriminator compares the generated data samples to actual data, the generator produces the data samples.

Variational Autoencoders (VAEs): VAEs produce data by first converting input data into a latent space, which is subsequently decoded accordingly. The process of creating new data points that resemble the input data works well.

Transformer-Based Models: These models, like GPT, create synthetic data by forecasting future data points based on previously acquired patterns by using massive datasets to understand data structures.​

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Generative AI

Beginner 5 Hours

Methods for Generating Synthetic Data

Statistical Distribution Methods

Methodology: Using real data, statistical distributions (such as normal and exponential) are identified through analysis. To produce a dataset that is statistically similar to the original, synthetic samples are then produced from these distributions.

Applications: useful for building datasets in which precise data points are not as important as the general statistical features.

Model-Based Methods

Methodology: Regression models and decision trees are examples of machine learning models that are trained on actual data in order to extract its properties. The artificial data produced by these models has the same statistical characteristics as the original data.

Applications: Suitable for creating hybrid datasets to improve model training by combining synthetic and real data.

Deep Learning Methods

Generative Adversarial Networks (GANs): GANs are made up of discriminator and generator neural networks, which collaborate to produce synthetic data that is realistic. While the discriminator compares the generated data samples to actual data, the generator produces the data samples.

Variational Autoencoders (VAEs): VAEs produce data by first converting input data into a latent space, which is subsequently decoded accordingly. The process of creating new data points that resemble the input data works well.

Transformer-Based Models: These models, like GPT, create synthetic data by forecasting future data points based on previously acquired patterns by using massive datasets to understand data structures.​

Related Tutorials

Frequently Asked Questions for Generative AI

Sequence of prompts stored as linked records or documents.

It helps with filtering, categorization, and evaluating generated outputs.



As text fields, often with associated metadata and response outputs.

Combines keyword and vector-based search for improved result relevance.

Yes, for storing structured prompt-response pairs or evaluation data.

Combines database search with generation to improve accuracy and grounding.

Using encryption, anonymization, and role-based access control.

Using tools like DVC or MLflow with database or cloud storage.

Databases optimized to store and search high-dimensional embeddings efficiently.

They enable semantic search and similarity-based retrieval for better context.

They provide organized and labeled datasets for supervised trainining.



Track usage patterns, feedback, and model behavior over time.

Enhancing model responses by referencing external, trustworthy data sources.

They store training data and generated outputs for model development and evaluation.

Removing repeated data to reduce bias and improve model generalization.

Yes, using BLOB fields or linking to external model repositories.

With user IDs, timestamps, and quality scores in relational or NoSQL databases.

Using distributed databases, replication, and sharding.

NoSQL or vector databases like Pinecone, Weaviate, or Elasticsearch.

With indexing, metadata tagging, and structured formats for efficient access.

Text, images, audio, and structured data from diverse databases.

Yes, for representing relationships between entities in generated content.

Yes, using structured or document databases with timestamps and session data.

They store synthetic data alongside real data with clear metadata separation.



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