Recently I read an article titled Train sklearn 100x faster, which is about an open-source Python module named sk-dist. The module implements a "distributed scikit-learn" by extending it’s built-in parallelisation of meta-estimator, such as, pipeline.Pipeline, model_selection.GridSearchCV, feature_selection.SelectFromModel and ensemble.BaggingClassifier, etc., using spark.

It was 1AM in the morning. Wise-men and women have told me not to stay up late and use computers. However, I have too sedentary life to sleep early, I am too bored with netflix and chill, and I am too sober to dream about the next big thing since tiktok. So, I did the next best thing…

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If you do not have the time to read the full article, consider reading the 30 seconds version.


If you have Machine Learning (ML) pipelines in production, you have to worry about backward compatibility of changes made to the pipeline. It may be tempting to increase test coverage, but a high test coverage cannot guarantee that your recent changes have not broken the pipeline or generated low quality results. To do that, you need to develop end-to-end tests that can be executed as part of the continuous integration pipelines. Developing such a test requires sampling the dataset that powers the…

Has anyone released open source libraries on these techniques?

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If you do not have time, here is the 30-second version:

  • If you or your team is doing a data science exploration work that is not a total waste, you need to preserve the work in such a way that you, your team, or someone else can get back to it later without too much trouble. The value of an idea starts from exploration and the value of exploration starts from sharing in such a way that it is easy to reproduce.
  • It may be tempting to refer to a notebook running in a platform accessible to you or your…

Misbah Uddin

Engineering Manager: AI, Analytics and Data @H&M. Opening little boxes, one at a time

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