Despite rigorously testing an ML model locally (on validation and test sets), it could be a terrible idea to instantly replace the previous model with the new model.
A more reliable strategy is to test the model in production (yes, on real-world incoming data).
While this might sound risky, ML teams do it all the time, and it isn’t that complicated.
There are many ways to do this.
We discussed five must-know strategies; how they work, when to use them, advantages and considerations, and their implementations in this week’s deployment deep dive: 5 Must-Know Ways to Test ML Models in Production (Implementation Included):
The article is entirely beginner-friendly, so even if you have not deployed any model before, you should be good to go.
Read it here: 5 Must-Know Ways to Test ML Models in Production (Implementation Included).
Thanks for reading!
An important update
Starting today, we have rolled out support for lifetime memberships (with purchasing power parity discount).
You can join here:
A lifetime member will retain access to the long articles for a lifetime, with no renewals, unlike the traditional monthly/yearly membership.
Also, implementing student discounts (50% off for students) has been a bit tricky with the service I use.
But I am working on it and will have an update for you in a few days.
If you are a student and want to join early, please mark your interest here:
Have a good day!
Avi