Model Monitoring AIQ

 

Model Monitoring

Model monitoring represents the activities necessary to ensure models are performing to specification and improving.

Why does Model Monitoring matter?

Live models often have problems if they aren't monitored on a regular basis resulting in a poor experience for end users. This includes on-going evaluation of the model against ground truth, analyzing model exhaust, and retraining models to improve performance, reduce drift and avoid bias.

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Machine Learning Monitoring

Actively monitoring machine learning models in production environments to ensure high reliability and quality for end users.

My organization is able to identify model issues in production such as drift.

Drift means a degradation in model performance as data distributions change over time.

My organization uses monitoring feedback to support continuous improvement of model performance.

Data Monitoring

Actively monitoring the data that supports machine learning models in production environments to ensure high reliability and quality for end users.

My organization performs error analysis to identify common types of model errors.

Model error analysis is performed to explore, understand, and isolate errors in model predictions, as well as the sources of those errors. This allows data scientists and data engineers to diagnose root causes and take corrective action.

Site Reliability Engineering

Strategies and processes in place to manage machine learning models and mitigate potential issues in production environments across the model lifecycle.

My organization monitors realtime KPIs for model performance such as latency and uptime.
My organization uses integrated monitoring across the full machine learning stack including models, data, pipelines, and infrastructure.

Monitoring Effectiveness

Having clearly defined roles and strategies defined and documented for monitoring machine learning models in production environments.

My organization manages a comprehensive machine learning monitoring framework including logging, alerts, dashboards, and visualizations.
My organization has clearly defined roles and responsibilities for monitoring production machine learning models.
My organization has defined and documented expectations for model performance.

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Model Monitoring - AIQ Capability Overview

Model monitoring represents the activities necessary to ensure models are performing to specification and improving.

Complete the AIQ assessment