Answers.Body: <p>You're correct that, if you keep a record of all the predictions you've made, then you can monitor drift when doing online predictions. However, you probably need an extra database to log the predictions, and also a periodic job which calculates drift reading off that database. That can make for a complicated setup.</p>
  <p>With batch predictions, if your batch is large enough, you can calculate summary statistics on that batch and detect drift at the same time as performing the prediction task. This is a lot easier to implement.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-05-10T20:58:36.550Z
Answers.LastActivityDate: 2022-05-10T20:58:36.550Z
Answers.UserDisplayName: Raphael Lopez Kaufman
Body: <p>In <a href="https://www.coursera.org/learn/managing-machine-learning-projects/lecture/29ZZA/online-learning-inference" rel="nofollow noreferrer">this video</a>, I learned that drift monitoring would be easier in batch prediction than that in online prediction:<br />
  <a href="https://i.sstatic.net/wMp8E.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/wMp8E.png" alt="enter image description here" /></a></p>
  <p>But I don't know why and I cannot find any information about it googling. In my opinion, in online prediction we only need to keep the prediction records and we have all the history data for drift analysis. What are the differences here between the two types of drift monitoring?</p>
  
Tags: <mlops><data-drift>
Title: How can batch prediction make drift monitoring easier than online prediction?
UserDisplayName: Lerner Zhang
ThreadText: # How can batch prediction make drift monitoring easier than online prediction?
  
  (Asked by: username_0 on 2022-05-10)
  
  In [this video](https://www.coursera.org/learn/managing-machine-learning-projects/lecture/29ZZA/online-learning-inference), I learned that drift monitoring would be easier in batch prediction than that in online prediction:  
  [![enter image description here](https://i.sstatic.net/wMp8E.png)](https://i.sstatic.net/wMp8E.png)
  
  But I don't know why and I cannot find any information about it googling. In my opinion, in online prediction we only need to keep the prediction records and we have all the history data for drift analysis. What are the differences here between the two types of drift monitoring?
  
  **Tags:** mlops, data-drift
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2022-05-10. Score: 1
  
  You're correct that, if you keep a record of all the predictions you've made, then you can monitor drift when doing online predictions. However, you probably need an extra database to log the predictions, and also a periodic job which calculates drift reading off that database. That can make for a complicated setup.
  
  With batch predictions, if your batch is large enough, you can calculate summary statistics on that batch and detect drift at the same time as performing the prediction task. This is a lot easier to implement.
  
  
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