Raoul-Gabriel Urma: Advanced Software Testing for Data Scientists | PyData New York 2019 |
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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 the engineering team is 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 the 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. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps |