Friends.
I am absolutely exhausted.
I am exhausted and a little tiny bit traumatized.
And you are somewhat to blame for this, as you will discover.
You will discover in a moment you will find out why.
But first, let's talk about what we have in store for today.
So we are going to be raising the bar on our baselines.
I'm just going to take a moment to remind you what you can already do.
Of course, generating text and generating code from combinations of frontier models with AI assistants,
with tools, and also open source systems using hugging face Transformers library, you can use Lang
chain to build a rag pipeline, and now you can also curate data and you can curate it.
Finally, if I may say so.
And you can also make a baseline model using some rather foolish techniques.
But then using traditional machine learning linear regression, including both feature engineering and
bag of words, and then onto more sophisticated techniques using word two vec and then adding in support
vector machines and then random forests.
Quite a trek it's been.
So today.
Today we are going to now take the framework we put together and put it against frontier models.
And this will sort of really capture all of the steps it takes to take a proper business problem, um,
understand the data and then present it to frontier models and compare their performance.
So it's an exciting moment for us, and we will get right to it.
And I will see you over at JupyterLab in a moment.