Overview
AI-ready Data Service (AirDS) is a component of large-scale model data services. In the deployment of enterprise AI applications, AirDS is as indispensable as air, providing essential AI data services. AirDS delivers precise and efficient AI data service support to global enterprises, encompassing data collection, cleaning, annotation, prompt engineering, and evaluation. Its objective is to enhance the effectiveness of enterprise AI applications and ensure that large AI model technologies are genuinely beneficial in industry implementations. AI ready for data, Data ready for AI.
Data Ready for AI:
- Data Collection and Cleaning: This includes sourcing raw data from multiple sources and enhancing data quality through deduplication, error correction, and filling in missing values. The data collection service covers multilingual and multimodal data, supporting the localization needs of enterprises expanding overseas.
- Data Annotation and Enhancement: This involves manually or automatically annotating data labels (such as classification, bounding boxes, region annotations, etc.) to provide "correct answers" for model training, particularly constructing high-quality annotated datasets tailored to vertical industry needs. The underlying infrastructure is built on AWS services like Bedrock and Claude.
AI Ready for Data: Prompt Engineering and Model Evaluation: This includes optimizing prompt design to improve the accuracy and utility of model outputs, and validating model performance through standardized evaluation tools. It also helps enterprises mitigate the "hallucination" issues of large models, enhancing the accuracy of application outcomes.
Highlights
- AI automatic annotation enhances data labeling efficiency; Boasts successful service cases across multiple industries; Customizable development tailored to clients' application scenarios.
- Enabling AI to better comprehend enterprise-level data, and ensuring enterprise-level data expansion meets AI requirements.
- In enterprise AI applications, AirDS empowers large models to understand user intentions and accomplish user tasks. It not only enables multimodal data recognition, including the transformation of unstructured data into structured formats, but also enhances the accuracy of voice interactions for end-users in multilingual environments.
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