In contemporary enterprises, the deployment of AI and machine learning technologies has become prevalent. Nonetheless, developing production-grade AI models often entails the challenge of converting unstructured data into high-quality training data. This process is both time-intensive and laborious, necessitating close collaboration between data science and business line teams. To mitigate these challenges, HaxiTAG studio has launched Q&A builder and Automatic labeling components to streamline data labeling and support LLM and GenAI applications.
Transformation Process of Enterprise Data into High-Quality Training Data
Data Collection and Cleaning
- Data Collection: Source data from diverse internal systems and external resources.
- Data Cleaning: Eliminate redundant data, rectify erroneous data, and standardize data formats to ensure quality.
Data Labeling
- Manual Labeling: Engage Subject Matter Experts (SMEs) for preliminary data labeling.
- Automatic Labeling: Employ HaxiTAG’s automatic labeling components to expedite the labeling process through predefined rules and machine learning models.
Data Transformation
- Structured Data Conversion: Convert labeled data into structured formats suitable for machine learning models.
- Data Augmentation: Enhance data diversity and volume through augmentation techniques.
Accelerating Data Labeling Methods
Capturing SME Knowledge
- Knowledge Base Construction: Develop an internal knowledge base to document and disseminate SMEs’ expertise and labeling practices.
- Knowledge Transfer Mechanism: Utilize HaxiTAG’s Q&A builder to convert SME knowledge into reusable data labeling functionalities.
Accelerating Large-Scale Data Labeling with LLM Prompts
- Prompt Design: Develop efficient prompts to guide LLM in accurate and efficient data labeling.
- Automated Labeling Process: Integrate LLM’s natural language processing capabilities to automate large-scale data labeling.
Measuring Label Accuracy and Iterative Improvement
- Accuracy Assessment: Regularly assess data labeling accuracy to maintain high-quality labels.
- Iterative Optimization: Refine labeling strategies and models based on assessment outcomes to continuously improve data quality.
Case Study A financial enterprise significantly improved data labeling efficiency and accuracy using HaxiTAG studio's solutions. Specific actions included:
- Introducing automatic labeling components, automating tasks previously requiring manual effort, and reducing workload by 50%.
- Establishing an internal knowledge base to capture and disseminate SME expertise, facilitating rapid onboarding of new employees for data labeling tasks.
- Leveraging LLM prompts to enhance the speed and accuracy of large-scale data labeling, resulting in a 30% increase in labeling accuracy.
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