Slides for my talk at PyData London 2022
"Clean Architecture: How to structure your ML projects to reduce technical debt"
PyData London returned with a three-day event at the usual place after a pandemic-related hiatus. I had the opportunity to present related to our key public mission:
“Increasing Machine Learning productivity through improved coding practices for Data Scientists.”
The recording of the talk is available on YouTube: https://www.youtube.com/watch?v=QXfsS-ZOeyA
The title of my talk:
Clean Architecture: How to structure your ML projects to reduce technical debt
I’ve been asked for the slides by multiple people. See them at the end of the post. I will share the recording as well when they are available.
I started with a (just to be clear - fake) quote from Clausewitz to the great amusement of the attendance:
Quick summary of slides:
What do we mean by “ML products”?
Why does tech debt matter in ML?
How ML Lifecycle affects tech debt?
Tech Debt vs Tech Mess (This slide was received by a significant amount of laughter)
What is refactoring?
What is Experimental-Operational Symmetry (EOS)?
What is decoupling?
Inversion of Control and Dependency Injection
Clean Architecture in Production
Three Useful Design Patterns (Adapter/Factory/Strategy)
Workflow building a system from scratch
Interoperability with Jupyter notebooks
Slides can be downloaded from here:
I write on the topic regularly; if you would like to read more on this, please take a look at my about page: [link]