WEBVTT 00:01.760 --> 00:02.480 Friends. 00:02.510 --> 00:05.390 I am absolutely exhausted. 00:05.390 --> 00:09.620 I am exhausted and a little tiny bit traumatized. 00:09.950 --> 00:13.520 And you are somewhat to blame for this, as you will discover. 00:13.550 --> 00:16.760 You will discover in a moment you will find out why. 00:16.790 --> 00:21.050 But first, let's talk about what we have in store for today. 00:21.500 --> 00:25.670 So we are going to be raising the bar on our baselines. 00:25.670 --> 00:29.360 I'm just going to take a moment to remind you what you can already do. 00:29.390 --> 00:36.230 Of course, generating text and generating code from combinations of frontier models with AI assistants, 00:36.230 --> 00:44.240 with tools, and also open source systems using hugging face Transformers library, you can use Lang 00:44.240 --> 00:51.110 chain to build a rag pipeline, and now you can also curate data and you can curate it. 00:51.110 --> 00:52.700 Finally, if I may say so. 00:52.700 --> 00:57.740 And you can also make a baseline model using some rather foolish techniques. 00:57.740 --> 01:04.040 But then using traditional machine learning linear regression, including both feature engineering and 01:04.040 --> 01:11.030 bag of words, and then onto more sophisticated techniques using word two vec and then adding in support 01:11.030 --> 01:14.270 vector machines and then random forests. 01:14.330 --> 01:16.250 Quite a trek it's been. 01:16.250 --> 01:17.750 So today. 01:17.780 --> 01:23.510 Today we are going to now take the framework we put together and put it against frontier models. 01:23.510 --> 01:30.080 And this will sort of really capture all of the steps it takes to take a proper business problem, um, 01:30.140 --> 01:35.750 understand the data and then present it to frontier models and compare their performance. 01:35.750 --> 01:41.870 So it's an exciting moment for us, and we will get right to it. 01:41.930 --> 01:46.130 And I will see you over at JupyterLab in a moment.