Versioning ML Models & Automating ML Pipelines Effectively using DVC & CML |

[@reachDeepNeuron]

Fantastic end to end explanation 👏👏👏

[@adityarathore736]

Great explanation. Thanks for making this video

[@pritam1102]

Nicely explained

[@chriswesterhold3412]

Great explanation. Thanks for putting this together.

[@imadityajadhav]

Great job Ashutosh.

[@DevSense19]

Aditya Jadhav Thank you Aditya

[@thamizhansudip6644]

Typically, we utilize ipynb notebooks, but can these notebooks be employed for deploying models, or is it obligatory to exclusively use .py files?

[@ravivarma5703]

Excellent explanation

[@DevSense19]

Thanks 😊

[@ramavathravi2719]

Nice video Bro..Keep it up

[@DevSense19]

Thank you so much 😀

[@pawanbhandarkar4199]

Excellent video! Thank you for sharing. Definitely deserves more views.
I am a complete DVC beginner and have a question: when you visualize dag, it looks like there is some conditional logic, where you have two paths: featurize->train->evaluate or just featurize->evaluate. But how is this decided? where is the conditional logic that defines this? I cant find anything in the yaml file that does this.

[@DevSense19]

dvc does analysis on inputs and outputs of each stage in yaml file and then creates dag

[@mohitsinghpawar9387]

It is an interesting video! Can it support APIs also ? Like a rest api with flask. If it .. please make a video on that or refer some articles . Thanks a lot

[@DevSense19]

Yeah it supports in every way. But it is suggestible to use this whenever data or model information is involved. If it is just code then you can use only git.

[@mohitsinghpawar5682]

@@DevSense19 thanks! Like how will it work for APIs then if we assume it is a simple flask app.py file with rest API

[@kritikabanerjee2346]

thankyou that helped

[@reachDeepNeuron]

Where is that containerization comes into play?? Where it can fit in the big picture?? Can i get your contact number , bro ??