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Issue 4: Resources to get an overview of a machine learning project

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Learn about data science in real life and machine learning in production
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It’s not easy to start when doing machine learning. This is why I would like to highlight some useful resources to get an idea of what a data science project looks like from end to end.

I also wanted to put forward a new tool to do data monitoring and tell you a story about debugging silent bugs in data science.

Machine learning engineering by Andriy Burkov

A book about machine learning in real life. From data preparation to deployment, you will learn how to handle a real data science project.

As a summary, I can highlight 2 ideas I like:

  • Validating your model is not only about evaluating your accuracy. You also must be sure that your model is ethical and will be appreciated and used by your end-users.
  • Feature engineering is important. Test it intensively.

ML Production pipelines: a classification example by Adriana Menegozzo

30 minutes of video to get an overview from end to end of how to productionalize models. It’s straightforward and concise.

As a summary: Test and monitor your predictive system. Don’t forget that productionalizing a model is not a one-shot but a progressing process. You will improve over time.

Awesome production machine learning

An awesome list of tools to productionalize models. If you’re looking for a tool, it might be there. And if you know a tool that is not in the list, you can open a pull request.

Very good to know.


Amazing! A tool to do data monitoring!

Evidently helps analyze machine learning models during development, validation, or production monitoring. The tool generates interactive reports from pandas DataFrame. Currently, the Data Drift report is available.

Data monitoring

Bugs are silent

As a summary: Bugs in predictive systems are most of the time silent. Don’t expect your users to raise their hands saying something is wrong. That won’t happen.

Use monitoring instead of waiting from your end-users!