Answers.Body: <p>Full disclosure: I work at Dessa, the company that developed this tech.</p>
  
  <p>We built a machine learning experiment management tool, called Atlas. The main feature is experiment management, allowing you to run thousands of experiments concurrently. This might help with your problem above <a href="https://github.com/dessa-oss" rel="nofollow noreferrer">https://github.com/dessa-oss</a></p>
  
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Answers.CreationDate: 2020-04-01T20:52:39.407Z
Answers.Text: Hi and welcome to AI SE! Thanks for sharing this info! Atlas looks promising and similar to comet.ml. What about prices? Is it free to use? Maybe you should edit your answer to describe these details.
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Answers.CreationDate: 2020-04-01T19:24:08.923Z
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Answers.Body: <p>Maybe you are looking for a combination of a <a href="https://en.wikipedia.org/wiki/Version_control" rel="nofollow noreferrer">version control system</a> (like <a href="https://git-scm.com/" rel="nofollow noreferrer">git</a> and <a href="https://github.com/" rel="nofollow noreferrer">Github</a>) and a tool like <a href="https://www.comet.ml/" rel="nofollow noreferrer">comet.ml</a>. In the past, I used comet.ml to keep track of different experiments performed with different hyper-parameters or different versions of the code. There are other alternatives to comet.ml, such as <a href="https://github.com/IDSIA/sacred" rel="nofollow noreferrer">sacred</a>, but they may also have different features and may not be as visually pleasing as comet.ml or even free. Personally, I liked comet.ml (even though, at the time, it still lacked some features). In any case, a VCS, like git, is widely used in software development (not just in AI projects) to keep track of different versions of the code, etc. You may also be interested in continuous integration (e.g. <a href="https://travis-ci.com/plans" rel="nofollow noreferrer">Travis CI</a>) and code review (e.g. <a href="https://app.codacy.com/login" rel="nofollow noreferrer">codacy</a>) tools.</p>
  
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Answers.UserDisplayName: nbro
Body: <p>During weeks and months of your work, many things may change, for example :</p>
  
  <ul>
  <li>You may modify the loss function</li>
  <li>Your training or validation datasets may change</li>
  <li>You modify data augmentation</li>
  </ul>
  
  <p>Which tools or processes do you use to track modifications you have made and how did they affected the model ?</p>
  
Tags: <neural-networks><machine-learning><ai-design>
Title: How to track performance of your model during experimenting?
UserDisplayName: user29467
ThreadText: # How to track performance of your model during experimenting?
  
  (Asked by: username_0 on 2020-01-23)
  
  During weeks and months of your work, many things may change, for example :
  
  * You may modify the loss function
  * Your training or validation datasets may change
  * You modify data augmentation
  
  Which tools or processes do you use to track modifications you have made and how did they affected the model ?
  
  **Tags:** neural-networks, machine-learning, ai-design
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2020-01-23. Score: 3 | Accepted answer
  
  Maybe you are looking for a combination of a [version control system](https://en.wikipedia.org/wiki/Version_control) (like [git](https://git-scm.com/) and [Github](https://github.com/)) and a tool like [comet.ml](https://www.comet.ml/). In the past, I used comet.ml to keep track of different experiments performed with different hyper-parameters or different versions of the code. There are other alternatives to comet.ml, such as [sacred](https://github.com/IDSIA/sacred), but they may also have different features and may not be as visually pleasing as comet.ml or even free. Personally, I liked comet.ml (even though, at the time, it still lacked some features). In any case, a VCS, like git, is widely used in software development (not just in AI projects) to keep track of different versions of the code, etc. You may also be interested in continuous integration (e.g. [Travis CI](https://travis-ci.com/plans)) and code review (e.g. [codacy](https://app.codacy.com/login)) tools.
  
  
  ---
  
  ### Answer by username_1 on 2020-04-01. Score: 1
  
  Full disclosure: I work at Dessa, the company that developed this tech.
  
  We built a machine learning experiment management tool, called Atlas. The main feature is experiment management, allowing you to run thousands of experiments concurrently. This might help with your problem above <https://github.com/dessa-oss>
  
  > **Comments:**
  > **username_2**: Hi and welcome to AI SE! Thanks for sharing this info! Atlas looks promising and similar to comet.ml. What about prices? Is it free to use? Maybe you should edit your answer to describe these details.  
  
  ---
  
  