So we've covered steps 1 to 4 of the five step strategy.
And that brings us to step five, which is productionize the one that maybe you haven't thought as much
about to date.
And this is about saying we built our model.
It's time for prime time.
We typically will start by defining an API that can be used to call our model to call our code.
That itself is either using a open source model or making a call to a frontier model with with various
scaffolding around it, like rag or prompt engineering.
We then decide how are we going to host and deploy our model, our API.
We address things like how it's going to be monitored, the information security concerns, how will
it scale up?
We then measure the business metrics that we had identified all the way back in step one and continuously
retrain, measure performance, and improve our model on an ongoing basis.
These are all part of the Productionize activity.
And so with that, where are we in our business problem that we've taken on to predict product prices.
So the first step understand we've done it.
We understood it.
We've talked about metrics.
We know what we've got to do.
Preparation is what we're doing right now.
We are in the middle of preparing our data set.
And then we need to select our model or models.
We need to customize.
And then finally we need to productionize.
So we will be going through all of those steps.
But we will be first of all finishing the second step preparation.
And with that let's go right now back to JupyterLab and get to preparing.