ML Data Operations Practice
Jump to navigation
Jump to search
A ML Data Operations Practice is a data management practice that focuses on managing and optimizing the data lifecycle for machine learning (ML) models.
- Context:
- It can (typically) include Data Acquisition, Data Annotation, Data Preprocessing, Model Training, Model Deployment, and Monitoring.
- It can (often) ensure data quality and integrity by implementing processes to maintain high-quality and accurate data for ML models.
- It can streamline Data Workflows (e.g. by developing efficient data pipelines for data acquisition, data processing, and data annotation.
- It can optimize model performance through effective data management.
- It can ensure compliance and security by adhering to legal and regulatory requirements, especially concerning data residency and privacy.
- It can design scalable solutions to handle increasing data volumes and complexities.
- It can range from being a manual process to a highly automated and scalable system.
- ...
- Example(s):
- a Legal Document Annotation System that showcases structured workflows and quality control measures.
- a Model Training Pipeline that demonstrates the integration of data preprocessing, feature engineering, and hyperparameter tuning.
- ...
- Counter-Example(s):
- Ad-hoc Data Management practices, which lack structured workflows and fail to ensure data quality and compliance.
- See: Data Pipeline, ML Model Deployment, Data Quality Assurance, Data Compliance