# Implementing Model Monitoring on Databricks

DevFeed: [Implementing Model Monitoring on Databricks](<https://devfeed.tech/articles/implementing-model-monitoring-on-databricks-28604.md>)

Original publisher: [Read original article](<https://www.marvelousmlops.io/p/lecture-10-implementing-model-monitoring>)

Author: Başak Tuğçe Eskili

Published: 2025-08-06T16:55:23Z

Content type: tutorial

Language: en

Sources: [MarvelousMLOps](<https://devfeed.tech/sources/marvelousmlops.md>)

Topics: [databricks](<https://devfeed.tech/topics/databricks.md>), [MLOps](<https://devfeed.tech/topics/mlops.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>), [model-serving](<https://devfeed.tech/topics/model-serving.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Logging](<https://devfeed.tech/topics/logging.md>), [Data Quality](<https://devfeed.tech/topics/data-quality.md>)

Tags: [databricks](<https://devfeed.tech/tags/databricks.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [model-monitoring](<https://devfeed.tech/tags/model-monitoring.md>), [model-serving](<https://devfeed.tech/tags/model-serving.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [request](<https://devfeed.tech/tags/request.md>), [table](<https://devfeed.tech/tags/table.md>)

## AI overview

Lecture 10 in an MLOps with Databricks course demonstrates model monitoring using inference tables and Lakehouse Monitoring. It covers collecting inference logs, creating a structured monitoring table, scheduling refreshes, and building a dashboard to visualize metrics and detect drift.

## Source excerpt

Lecture 10 of MLOps with Databricks course