# Powering ML-Based Systems With Reliable Data: The Data Annotation Journey

DevFeed: [Powering ML-Based Systems With Reliable Data: The Data Annotation Journey](<https://devfeed.tech/articles/powering-ml-based-systems-with-reliable-data-the-data-annotation-journey-28027.md>)

Original publisher: [Read original article](<https://tech.trivago.com/post/2022-09-01-powering-ml-based-systems-with-reliable-data-annotation/>)

Author: Omayma Said Senior Data Scientist @ trivago Linkedin profile

Published: 2022-09-01T00:00:00Z

Content type: tutorial

Language: en

Sources: [Trivago](<https://devfeed.tech/sources/trivago.md>)

Topics: [data](<https://devfeed.tech/topics/data.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [human feedback](<https://devfeed.tech/topics/human-feedback.md>), [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [development](<https://devfeed.tech/tags/development.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [quality](<https://devfeed.tech/tags/quality.md>)

## AI overview

This article explains why reliable data collection, curation, cleaning, and annotation are essential to building effective machine-learning systems. It introduces the data-centric AI movement and shares practical lessons from several data-annotation projects, including work at trivago.

## Source excerpt

In the last few years, organisations have been increasing their investments in building Machine Learning (ML) based systems. In practice, such systems often took longer than expected to be built...