Clean Architecture: How to Structure Your ML Projects to Reduce Technical Debt || Laszlo Sragner

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United States Coding Tech
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725,000
Published on ● Video Link: https://www.youtube.com/watch?v=zeubGVLH3Io


Duration: 36:47
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73

Software engineering principles are frequently mentioned as a solution to data science's productivity problem. Unfortunately, rarely in a comprehensive format to be actionable or adopted for data-intensive use.

In this talk, I will present a framework that enables practitioners to structure their projects and manage changes throughout the product lifecycle at low effort.

Audience will also learn about a minimum set of programming concepts to make this a reality.

The key takeaway for any Data Scientist is that you don't need to be a master programmer to start taking care of your own codebase.

PUBLICATION PERMISSIONS:
PyData provided Coding Tech with the permission to republish PyData talks.

CREDITS:
PyData YouTube channel: https://www.youtube.com/c/PyDataTV



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Tags:
machine learning
ml projects
architecture