# Introduction to MLOps

DevFeed: [Introduction to MLOps](<https://devfeed.tech/articles/introduction-to-mlops-28603.md>)

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

Author: Maria Vechtomova

Published: 2025-07-28T17:19:47Z

Content type: tutorial

Language: en

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

Topics: [MLOps](<https://devfeed.tech/topics/mlops.md>), [databricks](<https://devfeed.tech/topics/databricks.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [Testing](<https://devfeed.tech/topics/testing.md>)

Tags: [audit-trail](<https://devfeed.tech/tags/audit-trail.md>), [course](<https://devfeed.tech/tags/course.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [introduction](<https://devfeed.tech/tags/introduction.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [production](<https://devfeed.tech/tags/production.md>), [reproducibility](<https://devfeed.tech/tags/reproducibility.md>), [testing](<https://devfeed.tech/tags/testing.md>)

## AI overview

Lecture 1 of a hands-on MLOps with Databricks course explains what production means for machine-learning workflows. It uses a demand-forecasting example to show why scheduled notebooks may lack testing, monitoring, error handling, version control, deployment processes, rollback, and audit trails, and introduces MLOps principles for improving reliability and control.

## Source excerpt

Lecture 1 of MLOps with Databricks course