Architecture

Data Collection: Gather raw data from various sources, including text, images, audio, etc.
Data Cleaning: Remove duplicates, irrelevant or erroneous data, and handle missing or anomalous values.
Data Annotation: Annotate the data as needed to ensure accuracy and consistency.

Initial Data Filtering

Data Preprocessing: Perform preprocessing steps such as normalization, standardization, or tokenization.
Data Classification: Classify the data based on features and labels to perform initial filtering and identify potentially useful datasets.

Define Evaluation Metrics: Establish metrics for assessing data quality, such as accuracy, completeness, consistency, and relevance.
Develop Evaluation Models: Create or select appropriate evaluation models, which may include rule-based systems, statistical methods, or machine learning models.
- Rule-Based Evaluation: Set rules and standards to assess data quality.
- Statistical Methods: Use statistical methods to detect anomalies and biases in the data.
- Machine Learning Models: Train machine learning models to evaluate data quality.
Execute Evaluation: Apply the evaluation models to the data, generating quality reports and scores.
Feedback Loop: Adjust data collection and processing workflows based on evaluation results to improve data quality.

Filter Based on Evaluation Results: Select high-quality datasets based on evaluation outcomes.
Data Integration: Integrate the filtered datasets into the training dataset, ensuring consistency and high quality.

Last updated 9 months ago

Data Collection: Gather raw data from various sources, including text, images, audio, etc.
Data Cleaning: Remove duplicates, irrelevant or erroneous data, and handle missing or anomalous values.
Data Annotation: Annotate the data as needed to ensure accuracy and consistency.

Data Preprocessing: Perform preprocessing steps such as normalization, standardization, or tokenization.
Data Classification: Classify the data based on features and labels to perform initial filtering and identify potentially useful datasets.

Define Evaluation Metrics: Establish metrics for assessing data quality, such as accuracy, completeness, consistency, and relevance.
Develop Evaluation Models: Create or select appropriate evaluation models, which may include rule-based systems, statistical methods, or machine learning models.
- Rule-Based Evaluation: Set rules and standards to assess data quality.
- Statistical Methods: Use statistical methods to detect anomalies and biases in the data.
- Machine Learning Models: Train machine learning models to evaluate data quality.
Execute Evaluation: Apply the evaluation models to the data, generating quality reports and scores.
Feedback Loop: Adjust data collection and processing workflows based on evaluation results to improve data quality.

Filter Based on Evaluation Results: Select high-quality datasets based on evaluation outcomes.
Data Integration: Integrate the filtered datasets into the training dataset, ensuring consistency and high quality.

Last updated 9 months ago