The journey to deploy a model to production starts with testing it rigorously, including its code implementation. In this tutorial, you will learn about state of the art software testing approach. You will learn how to write unit tests with enhanced diagnostics, leverage validation tools from numpy, pandas, scikit-learn, apply test doubles and generate test cases using property-based testing.
It's fun to develop a model in a Python notebook! But engineering team are always complaining about code maintenance and code quality, asking for production ready code. What can you as a data scientist learn from the software development world to help with this? In this tutorial, you will learn about state of the art testing approach. You will learn how to break down a model implemented in a notebook into separate parts which you can unit test and ensure quality with common tools available in Python. In addition, you will learn how to apply property based testing and test doubles.
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