# Introduction to ML monitoring

DevFeed: [Introduction to ML monitoring](<https://devfeed.tech/articles/introduction-to-ml-monitoring-28602.md>)

Original publisher: [Read original article](<https://www.marvelousmlops.io/p/introduction-to-ml-monitoring>)

Author: Başak Tuğçe Eskili

Published: 2025-08-05T21:56:29Z

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>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [article](<https://devfeed.tech/tags/article.md>), [course](<https://devfeed.tech/tags/course.md>), [data](<https://devfeed.tech/tags/data.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [errors](<https://devfeed.tech/tags/errors.md>), [introduction](<https://devfeed.tech/tags/introduction.md>), [latency](<https://devfeed.tech/tags/latency.md>), [ml](<https://devfeed.tech/tags/ml.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [model](<https://devfeed.tech/tags/model.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [performance](<https://devfeed.tech/tags/performance.md>), [predictions](<https://devfeed.tech/tags/predictions.md>), [production](<https://devfeed.tech/tags/production.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

## AI overview

An introductory lecture in the MLOps with Databricks course explains why machine learning systems require monitoring beyond system health, errors, latency, KPIs, and infrastructure costs. It introduces data drift and concept drift as causes of model performance degradation, even when code and infrastructure remain unchanged.

## Source excerpt

Lecture 9 of MLOps with Databricks course