---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 33d25b008ea9627e480a4a2a38771a7071d774c21e79fdcb86c7cb721b53776f
  pageDirectory: concepts
  sources:
    - machine-learning-lifecycle-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - production-ml-monitoring-with-inference-tables
    - PMMWIT
    - Model Monitoring with Inference Tables
    - Monitoring Served Models with Inference Tables
  citations:
    - file: machine-learning-lifecycle-databricks-on-aws.md
title: Production ML Monitoring with Inference Tables
description: Continuous monitoring of production ML systems using inference tables to log model inputs/outputs automatically, combined with data quality monitoring, drift detection, and anomaly alerts.
tags:
  - machine-learning
  - monitoring
  - mlops
timestamp: "2026-06-19T19:19:57.163Z"
---

# Production ML Monitoring with Inference Tables

**Production ML Monitoring with Inference Tables** refers to the practice of automatically logging model inputs and outputs from deployed models into Delta tables managed by Unity Catalog, enabling continuous monitoring of model performance, data quality, and drift over time.

## Overview

Production ML systems can degrade over time as user behavior shifts or data pipelines change. To detect and respond to these changes, organizations need to continuously monitor production data and model predictions. Inference tables provide a mechanism for automatic logging of model inputs and outputs without requiring changes to model code. ^[machine-learning-lifecycle-databricks-on-aws.md]

## How Inference Tables Work

For real-time serving, inference tables capture the inputs and outputs of deployed models automatically. This logging happens transparently — the model code does not need to be modified to enable monitoring. For batch serving, the pipelines naturally read from and write to Delta tables managed by Unity Catalog, providing built-in observability. ^[machine-learning-lifecycle-databricks-on-aws.md]

## Monitoring Capabilities

Once inference data is logged, it feeds into several monitoring workflows:

- **Data quality monitoring**: Track data quality metrics on incoming production data. ^[machine-learning-lifecycle-databricks-on-aws.md]
- **Feature drift detection**: Monitor how the distribution of input features changes over time compared to training data. ^[machine-learning-lifecycle-databricks-on-aws.md]
- **Prediction distribution monitoring**: Track shifts in model output distributions that may indicate changing conditions. ^[machine-learning-lifecycle-databricks-on-aws.md]
- **Prediction quality metrics**: If ground truth or feedback data is available, join this data with serving logs to compute accuracy, precision, recall, or other quality metrics. ^[machine-learning-lifecycle-databricks-on-aws.md]

## Alerting and Incident Response

The monitoring system provides a monitoring UI for visualizing metrics and detecting anomalies. [Anomaly Detection Alerts](/concepts/anomaly-detection-alert.md) can be configured to trigger escalation or automated retraining before quality noticeably degrades. ^[machine-learning-lifecycle-databricks-on-aws.md]

## Integration with the ML Lifecycle

Inference table monitoring connects to earlier stages of the [machine learning lifecycle](/concepts/cicd-for-machine-learning.md). The metrics defined during development and training can be reused as monitoring metrics in production. This creates a closed loop: monitoring data informs retraining decisions, and retrained models are deployed through the same staging and production pipeline. ^[machine-learning-lifecycle-databricks-on-aws.md]

## Related Concepts

- Machine Learning Lifecycle — The end-to-end journey from scoping to production monitoring
- [MLflow Model Registry](/concepts/mlflow-model-registry.md) — Model versioning and lifecycle management
- [Model Serving](/concepts/model-serving.md) — Real-time and batch inference patterns
- [Data Quality Monitoring](/concepts/data-quality-monitoring.md) — Tracking data quality over time
- [Drift Detection](/concepts/data-drift-detection.md) — Identifying distribution shifts in features and predictions
- [Unity Catalog](/concepts/unity-catalog.md) — Governance layer for data and models
- MLOps Workflows — Production ML operations reference

## Sources

- machine-learning-lifecycle-databricks-on-aws.md

# Citations

1. [machine-learning-lifecycle-databricks-on-aws.md](/references/machine-learning-lifecycle-databricks-on-aws-d195211f.md)
