Thuật ngữ AI
Từ điển đầy đủ về Trí tuệ nhân tạo
Model Lifecycle Management
Set of processes and tools for managing the complete lifecycle of a machine learning model, from its initial design to its deployment and final retirement. This systematic approach ensures traceability, reproducibility, and continuous maintenance of models throughout their operational existence.
Model Deployment
Process of integrating a machine learning model into a production environment where it can generate real-time or batch predictions. This critical step includes infrastructure configuration, API exposure, and setting up auto-scaling mechanisms.
Model Retraining
Process of periodically or triggered retraining of a model with new data to maintain or improve its performance in response to evolving data patterns. Automatic retraining uses CI/CD pipelines adapted for machine learning to ensure the continued relevance of the model.
Continuous Deployment for ML
Automation of the deployment process of machine learning models to production after successful validation in testing environments. This practice enables rapid delivery of model improvements while maintaining rigorous safeguards for quality and security.
A/B Testing for Models
Experimental methodology for comparing the performance of multiple model versions in production by directing a portion of traffic to each version. A/B testing for models provides objective quantitative metrics to select the best version based on business performance indicators.
Model Performance Metrics
Quantitative indicators for evaluating the quality and effectiveness of a machine learning model, including accuracy, recall, F1-score, AUC-ROC, and specific business metrics. These measures are essential for model validation, monitoring, and selection.
Model Retirement
Planned process of decommissioning a machine learning model that has become obsolete, ineffective, or replaced by a more performing version. Model retirement includes dependency migration, data archiving, and stakeholder communication.
Model Validation
Rigorous evaluation process of a machine learning model before its deployment to production to verify its performance, robustness, and compliance with business requirements. Validation includes testing on holdout data, cross-validation, and evaluation of edge cases.
Model Packaging
Process of preparing a machine learning model for deployment by creating a self-contained container that includes the model, its dependencies, configurations, and inference APIs. Packaging ensures the model's portability and reproducibility across different runtime environments.
Model Staging
Isolated intermediate environment where models are deployed for final testing before going into production, faithfully replicating operational conditions. Staging allows for validating the model's integrations, performance, and behavior in a realistic context without impacting end users.