- [Instructor] In this video, we'll discuss
how a ML.NET project can be stored in Source Control.
Because the ML.NET model is built
using Visual Studio and C#,
it can be checked into source control
using the project structure set up in a repo,
whether that be GitHub or Azure DevOps.
In addition, after the model is trained,
it can then be saved as a .zip file.
This .zip file can also be stored in the repo as well.
The one thing to note of is the training data
that's going to be used for that model.
The data used for training each version
of the model should be documented.
Obviously, you can't store thousands of records,
but you can document date ranges
or some other identifiers for the records used in training.
Some may choose to save the data as a database snapshot.
Others may like to save it in an extracted file.
This is all dependent on your team's convention.
In addition, the data that's used for training
should be documented.
This could be entered as a comment when checking in changes
into the repo, or it could be in a separate document
that's maintained in the solution.
As a backup machine learning.net provides the ability
to extract parameters from a model.
These parameters include weights and bias,
so if the data is not available,
or is not documented correctly,
the parameters can be extracted from that model.
However, it is always good practice to document the data
that was used for the training,
not only for auditing purposes,
but also for troubleshooting in case you end up
with the results that are unexpected.