How to structure your ML project code

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[@mehrdat]

Concise, to the point and very useful. I am learning and enjoying your videos. Do you accept any ideas for the videos?

[@ufuoma833]

Just what I need. Thank you.

[@nobir98]

This video is good no question about it and thank you for this awesome video but I humble request to the author of this video that please do not do the transition video sound like 1:22. IDK but it hurts my ears.

[@realworldml]

Thanks for the feedback

[@ArifMarias]

Wondering, do you have any video on that where you have applied all of these 3 tips with end to end small project? If you've, can you please share the links with us. Btw, you're contents are always awesome! Thanks a million.

[@realworldml]

Thanks for your feedback. I am working on a new hands on course on real time ml that will cover these 3 tips, among other things.

[@samuelantunes-q8i]

Dude amazing content! have you tried the new uv python packaging that uses rust? Wishing you the best!

[@realworldml]

Thanks for the feedback. I havent tried uv yet, but looks promising. Rust tooling is like fresh air in thr Python world

[@ananthakrishnank3208]

Thank you! Your website from description is cool as well.

[@realworldml]

Glad you like it!

[@hamidoshaat]

Can you do one complete project for MLOps ( development and deployment) ?

[@realworldml]

Yes, here it is -> realworldmachinelearning.carrd.co

[@letgoofwill]

Amazing tips !! Thank you!

[@realworldml]

Glad it was helpful!

[@recs8564]

Amazing video brother!

[@realworldml]

Was it helpful?

[@nikhilsastry6631]

Awesome

[@pondupondu9131]

Why not Google Colab?

[@realworldml]

Same code structure applies whether you develop in a colab runtime, your local laptop or a paid AWS instance

[@dinoscheidt]

Jupyter is a one dimensional document. Software has teste, code coverage, functions and methods share between different parts of the system etc etc Jupyter is great for presenting runnable results. But not maintainable code bases.