Entities and Attributes of Machine Learning Applications
In database design, entities represent real-world objects or concepts, while attributes describe their characteristics or properties. For a machine learning 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 rows and columns.
Features and Labels
- FeatureID (Primary Key): Unique identifier for each feature.
- Name: Name or description of the feature.
- Type: Type of the feature (e.g., numerical, categorical, text).
- DatasetID (Foreign Key): Reference to the dataset containing the feature.
- Label: Indicator variable or outcome variable for supervised learning tasks.
Model
- ModelID (Primary Key): Unique identifier for each machine learning model.
- Name: Name or description of the model.
- Algorithm: Machine learning algorithm used for model training.
- Hyperparameters: Hyperparameters tuned during model training.
- Performance: Performance metrics evaluated on the model (e.g., accuracy, loss).
How to Design Database for Machine Learning Applications
Machine learning (ML) has emerged as a transformative technology, enabling computers to learn from data and make predictions or decisions without being explicitly programmed.
Behind every successful machine learning application lies a robust database architecture designed to store, manage, and analyze large volumes of data efficiently.
In this article, we’ll explore the intricacies of designing databases specifically tailored for machine learning applications.