Generative AI Tutorials

Generative AI - Data Collection and Preprocessing

Data Collection and Preprocessing

Data Collection

A generative model's performance is essentially reliant on the caliber and variety of the training data. Strategies for gathering data that work well include:

  • Web Crawling and Scraping: Web scraping and crawling are automated methods for collecting massive volumes of data from several online sources. Although this approach guarantees a broad dataset, it must be used carefully to prevent moral and legal dilemmas.
  • Pre-existing Datasets: Using popular datasets that have already undergone cleaning and formatting in preparation for a given purpose. LibriSpeech provides audio data, ImageNet provides pictures, and the Common Crawl dataset provides text data.
  • Custom Data Collection: Organizations can use surveys, trials, or proprietary databases to gather data on their own behalf in order to meet particular demands. Although this method guarantees data relevance, it may need a lot of resources.

Data Preprocessing

Raw data is preprocessed to provide a clear, organized format that is appropriate for model training. Important preliminary actions are:

  • Data cleaning: It is the process of getting rid of duplicates, fixing mistakes, and dealing with missing information. Among the methods are imputation, normalization, and deduplication. For example, deduplication eliminates redundant data to mitigate model bias towards recurring patterns.​
  • Tokenization: Breaking up text into smaller chunks (tokens) makes it easier for models to understand. This technique is known as tokenization. Advanced tokenization techniques, such as Byte Pair Encoding (BPE), aid in effectively managing vocabulary size and preserving semantic meaning.
  • Data augmentation: It involves producing additional data points for the dataset using methods such as text paraphrase, picture rotation and flipping, and audio pitch manipulation. This enhances the robustness and generalizability of the model.

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Generative AI

Beginner 5 Hours

Data Collection and Preprocessing

Data Collection

A generative model's performance is essentially reliant on the caliber and variety of the training data. Strategies for gathering data that work well include:

  • Web Crawling and Scraping: Web scraping and crawling are automated methods for collecting massive volumes of data from several online sources. Although this approach guarantees a broad dataset, it must be used carefully to prevent moral and legal dilemmas.
  • Pre-existing Datasets: Using popular datasets that have already undergone cleaning and formatting in preparation for a given purpose. LibriSpeech provides audio data, ImageNet provides pictures, and the Common Crawl dataset provides text data.
  • Custom Data Collection: Organizations can use surveys, trials, or proprietary databases to gather data on their own behalf in order to meet particular demands. Although this method guarantees data relevance, it may need a lot of resources.

Data Preprocessing

Raw data is preprocessed to provide a clear, organized format that is appropriate for model training. Important preliminary actions are:

  • Data cleaning: It is the process of getting rid of duplicates, fixing mistakes, and dealing with missing information. Among the methods are imputation, normalization, and deduplication. For example, deduplication eliminates redundant data to mitigate model bias towards recurring patterns.​
  • Tokenization: Breaking up text into smaller chunks (tokens) makes it easier for models to understand. This technique is known as tokenization. Advanced tokenization techniques, such as Byte Pair Encoding (BPE), aid in effectively managing vocabulary size and preserving semantic meaning.
  • Data augmentation: It involves producing additional data points for the dataset using methods such as text paraphrase, picture rotation and flipping, and audio pitch manipulation. This enhances the robustness and generalizability of the model.

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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