I realized my enthusiasm for this project is a bit insane, but I have to tell you that I am very satisfied
with the outcome.
It's rare that you come up with a project like this that has so many moving parts and is technically
quite complex and has a lot of substance to it, but have such a simple and tangible outcome as our
push notification for a good deal.
There is something beyond the king of the notification that really makes this very satisfying indeed.
But one point that I wanted to make about it, perhaps a more, more seriously, is to point out that
this sort of template that we've got here for solving this problem is something that you can apply to
a lot of other similar kinds of business problems and startup ideas and commercial problems that you
might even have been thinking about already.
I mean, to give you something that comes to mind right away is that when I was looking on hugging face
for the data for this this project, for the Amazon pricing data, I also came across a bunch of data
that that has company reports, annual reports from companies and shows how that relates to the change
in share price on the day and the few days after the report comes out, which is a very interesting
data set.
And it occurs to me that whilst this is more of a stretch, it is also obviously a bigger opportunity
if you can find a way to crack it.
If there's a way to build a proprietary model that's able to look at that and make some prediction about
what will happen to the share price as a result of a company's report being published.
And you could imagine then having a scanner agent that scans the new company reports being being being
released.
And so you can really see the very similar agent workflow that you would adapt for that kind of of of
problem.
Now, of course, I will tell you that any trading decisions that you make are entirely at your own
risk, and I have nothing to do with them at all.
And obviously no one has yet cracked that problem of being able to build an automated AI system that
can do things like read company reports and make, uh, trading decisions robust trading decisions as
a result.
But it seems like a serious, a very, um, important challenge to take on.
And I wanted to point that out because it shows you how similar that is in terms of the the bits and
pieces of the puzzle that you would look at if you wanted to do that.
So that could certainly be a big challenge, if nothing else, just for the interest of seeing what
happens and seeing whether you can find signal in that kind of data and using it as an opportunity to
build a similar agent workflow.
Okay.
Anyway, with that, let's move to the last slide of this week, which is of this sorry, not of this
week, of this day, which is to tell you that there is now one day left.
There is one final day, week eight, day five, which will be the conclusion of this course.
And you don't want to miss it.
Uh, before I tell you what you'll do, then just to remind you, at this point you can generate code
and text with frontier models, with open source models, using hugging face, using APIs with tools,
structured outputs, rag with assistance from from Gradio.
You can follow that strategy we went through to solve problems including curating your data, which
is hard and grueling, but perhaps the single most important step.
Um, making your baseline model and fine tuning a frontier model.
And then you can Select and train an open source model.
And we did it.
We beat the frontier when we did that.
You can deploy models to production.
Now you can use modal to to put something up there on the cloud.
You can also productionize your code in other ways and prepare it into something that can be run in
production environments.
And now you can build agentic workflows with planning memory, databases and multiple agents working
together to take a complex task and break it down.
And so that brings us to preparing for tomorrow.
Tomorrow, of course, we're going to be building a user interface, which is going to be glorious because
it's in Gradio.
Uh, we'll also be looking at the fact that today when we ran that workflow, you'll notice that it
ran and then it completed.
So it's not really autonomous and it doesn't keep running and have it still relies on the human to press
it each time.
So we need to fix that of course.
So we will do both of those things tomorrow.
And once they are done, that will leave you in a position where you will have mastered AI and LM engineering.
And I can't wait for that point.
I hope that you are both excited and proud and I will see you there.