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Automated ML Model Testing and Validation: Show HN(mymlportal.ai)

340 points by ai_quality 6 months ago flag hide 21 comments

testuser2 6 months ago next
I've also been working on automated ML model testing and validation. My team has been using Great Expectations and TensorFlow Model Analysis. It's been working great for us.
- original_poster 6 months ago next
  Thanks for sharing! I'll have to check those out. I'm always looking for new tools to try out.
- testuser3 6 months ago prev next
  Yeah, I've heard good things about Great Expectations. How do you find TensorFlow Model Analysis compared to other tools you've used?
  testuser2 6 months ago next
  I really like TensorFlow Model Analysis. It's very user-friendly and it's helped us catch a lot of issues with our models. I find it to be more intuitive than some other tools I've used.
testuser1 6 months ago prev next
Great article! I've been struggling with ML model testing and validation and this really helps. Thanks for sharing!
- original_poster 6 months ago next
  Thanks for the kind words! I'm glad it's helpful. Happy to answer any questions.
- anotheruser 6 months ago prev next
  Just curious, what tools did you use for automated testing and validation?
  testuser1 6 months ago next
  I used pytest and cross_val for the testing and validation. It's a great combo!
  anotheruser 6 months ago next
  Awesome, I'll have to give that a try!
testuser4 6 months ago prev next
Has anyone tried using Kubeflow for automated testing and validation?
- original_poster 6 months ago next
  I haven't personally used Kubeflow for testing and validation, but I've heard good things about it for ML workloads in general. It could be worth checking out.
- testuser3 6 months ago prev next
  I've used Kubeflow for other things and it's a great platform. I can see how it would be useful for testing and validation.
testuser5 6 months ago prev next
Have you considered using continuous integration (CI) pipelines for your automated testing and validation?
- original_poster 6 months ago next
  Yes, we actually use GitHub Actions for our CI pipeline. It works well for us and helps us catch issues early on in the development process.
- testuser6 6 months ago prev next
  We use GitLab CI for our CI pipeline and it's been working great for us. It integrates well with our other tools and processes.
testuser7 6 months ago prev next
Does anyone have any tips for writing effective unit tests for ML models?
- original_poster 6 months ago next
  I find it helpful to test for both expected outputs and expected errors. It's also important to test for edge cases and to make sure your test data is representative of your actual data.
- testuser8 6 months ago prev next
  I like to use a test-driven development (TDD) approach. That way, I'm forced to think about the tests I want to write before I start working on the code. It helps ensure that I don't forget any important tests along the way.
testuser9 6 months ago prev next
What are some common pitfalls to avoid when it comes to automated ML model testing and validation?
- original_poster 6 months ago next
  One common mistake is not testing for edge cases. It's important to make sure your tests cover all possible inputs, including ones that might be less common. Another common pitfall is not testing for both expected outputs and expected errors. It's crucial to make sure your model behaves as expected even when it encounters unexpected inputs.
- testuser10 6 months ago prev next
  I've found that it's also important to make sure your test data is representative of your actual data. It's easy to write tests using idealized data, but that might not reflect the real-world inputs your model will encounter.

testuser2 6 months ago next
I've also been working on automated ML model testing and validation. My team has been using Great Expectations and TensorFlow Model Analysis. It's been working great for us.
- original_poster 6 months ago next
  Thanks for sharing! I'll have to check those out. I'm always looking for new tools to try out.
- testuser3 6 months ago prev next
  Yeah, I've heard good things about Great Expectations. How do you find TensorFlow Model Analysis compared to other tools you've used?
  testuser2 6 months ago next
  I really like TensorFlow Model Analysis. It's very user-friendly and it's helped us catch a lot of issues with our models. I find it to be more intuitive than some other tools I've used.
testuser1 6 months ago prev next
Great article! I've been struggling with ML model testing and validation and this really helps. Thanks for sharing!
- original_poster 6 months ago next
  Thanks for the kind words! I'm glad it's helpful. Happy to answer any questions.
- anotheruser 6 months ago prev next
  Just curious, what tools did you use for automated testing and validation?
  testuser1 6 months ago next
  I used pytest and cross_val for the testing and validation. It's a great combo!
  anotheruser 6 months ago next
  Awesome, I'll have to give that a try!
testuser4 6 months ago prev next
Has anyone tried using Kubeflow for automated testing and validation?
- original_poster 6 months ago next
  I haven't personally used Kubeflow for testing and validation, but I've heard good things about it for ML workloads in general. It could be worth checking out.
- testuser3 6 months ago prev next
  I've used Kubeflow for other things and it's a great platform. I can see how it would be useful for testing and validation.
testuser5 6 months ago prev next
Have you considered using continuous integration (CI) pipelines for your automated testing and validation?
- original_poster 6 months ago next
  Yes, we actually use GitHub Actions for our CI pipeline. It works well for us and helps us catch issues early on in the development process.
- testuser6 6 months ago prev next
  We use GitLab CI for our CI pipeline and it's been working great for us. It integrates well with our other tools and processes.
testuser7 6 months ago prev next
Does anyone have any tips for writing effective unit tests for ML models?
- original_poster 6 months ago next
  I find it helpful to test for both expected outputs and expected errors. It's also important to test for edge cases and to make sure your test data is representative of your actual data.
- testuser8 6 months ago prev next
  I like to use a test-driven development (TDD) approach. That way, I'm forced to think about the tests I want to write before I start working on the code. It helps ensure that I don't forget any important tests along the way.
testuser9 6 months ago prev next
What are some common pitfalls to avoid when it comes to automated ML model testing and validation?
- original_poster 6 months ago next
  One common mistake is not testing for edge cases. It's important to make sure your tests cover all possible inputs, including ones that might be less common. Another common pitfall is not testing for both expected outputs and expected errors. It's crucial to make sure your model behaves as expected even when it encounters unexpected inputs.
- testuser10 6 months ago prev next
  I've found that it's also important to make sure your test data is representative of your actual data. It's easy to write tests using idealized data, but that might not reflect the real-world inputs your model will encounter.