Entities and Attributes of AI Applications
In database design, entities represent real-world objects or concepts, while attributes describe their characteristics or properties. For an AI application, common entities and their attributes include:
Dataset
- DatasetID (Primary Key): Unique identifier for each dataset.
- Name: Name or description of the dataset.
- Source: Source of the dataset (e.g., database table, CSV file, API).
- Size: Size of the dataset in terms of samples and features.
Data Samples:
- SampleID (Primary Key): Unique identifier for each data sample.
- DatasetID (Foreign Key): Reference to the dataset containing the sample.
- Data: Raw data or features of the sample (e.g., text, images, sensor readings).
- Label: Target label or category of the sample for supervised learning tasks.
Model:
- ModelID (Primary Key): Unique identifier for each AI model.
- Name: Name or description of the model architecture.
- Algorithm: AI algorithm used for model training or analysis.
- Hyperparameters: Parameters tuned during model training.
- Performance: Performance metrics evaluated on the model (e.g., accuracy, loss).
How to Design Databases for Artificial Intelligence Applications
Artificial intelligence (AI) applications encompass a wide range of technologies, from machine learning and natural language processing to computer vision and robotics.
Behind every successful AI application lies a robust database architecture designed to store, manage, and analyze vast amounts of data efficiently.
In this article, we’ll delve into the intricacies of designing databases specifically tailored for artificial intelligence applications.