// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Model Drift
Model drift happens when a deployed model's performance gets worse over time because the real-world data it sees has changed.
TECHNICAL DEFINITION
Model drift, also known as performance drift, refers to the degradation of a machine learning model's predictive accuracy or effectiveness in a production environment due to changes in the underlying data distribution or relationships between features and targets.
BACKGROUND
AI observability is the practice of collecting and analyzing telemetry, data such as logs, metrics, and traces that a system automatically records as it runs, from artificial intelligence systems deployed in production, in order to understand how they behave and how well they perform in terms of accuracy, cost, and safety. It applies the idea of observability, which comes from control theory and software engineering, to systems based on machine learning models, and in particular to large language models (LLMs) and autonomous agents. In control theory, observability is a measure of how well the internal state of a system can be inferred from its external outputs.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Performance drift
- model decay
- accuracy degradation
USAGE NOTE
Model drift necessitates retraining or updating the deployed model.
DEVELOPERS
Organizations developing technology related to Model Drift.
Specializes in AI observability, offering tools for detecting and diagnosing model drift, data quality issues, and performance degradation in production machine learning models.
Provides an AI observability platform for monitoring, explainability, and fairness, with robust capabilities to detect and analyze model drift, data drift, and data integrity issues.
Offers an AI performance monitoring platform that helps detect and diagnose model drift, data quality issues, and other performance problems for production AI systems, ensuring model reliability.
Their Vertex AI platform includes MLOps capabilities for monitoring machine learning models in production, with features specifically designed to detect and alert on model drift and data drift.
Azure Machine Learning offers comprehensive MLOps tools, including model monitoring services that help detect drift in model predictions and input data, crucial for maintaining model performance.
AWS SageMaker Model Monitor provides continuous monitoring of ML models in production to detect data and model quality issues, including drift, and offers explainability for identified issues.
An MLOps platform used for experiment tracking, model versioning, and production monitoring, offering dashboards and alerts for identifying performance issues and drift in machine learning models.
Specializes in MLOps, providing enterprise-grade solutions for deploying, managing, and monitoring machine learning models in production, including advanced drift detection capabilities.