WEBVTT 00:00.470 --> 00:01.100 Well. 00:01.130 --> 00:02.000 Fantastic. 00:02.030 --> 00:06.560 It's coming up to the end of the week, and that means it's coming up to a challenge for you again, 00:06.560 --> 00:10.580 even though I've just given you a challenge to build a Gradio user interface for for what we just saw. 00:10.580 --> 00:11.750 But that's an easy challenge. 00:11.750 --> 00:12.680 You can do that. 00:12.680 --> 00:13.640 No problem. 00:13.640 --> 00:14.990 This you need a harder challenge. 00:14.990 --> 00:17.360 At the end of the week, it's time for a harder challenge. 00:17.360 --> 00:25.130 So the end of week challenge is to build an important business application that we will, in fact, 00:25.130 --> 00:26.690 use later in the course. 00:26.690 --> 00:32.120 Although yeah, you you won't need to to you won't need to have built it for that because because I'll 00:32.120 --> 00:32.750 have done it. 00:32.750 --> 00:35.690 But it's really helpful if you've done it. 00:35.690 --> 00:39.680 And this is something that you'll be able to use no matter in any business. 00:39.710 --> 00:45.260 This, this, this tool will apply to every business vertical and can be useful to you, I guarantee 00:45.290 --> 00:45.830 it. 00:46.220 --> 00:48.140 And this is what it is. 00:48.140 --> 00:58.940 Create your own tool that generates synthetic testing data a test data generator, open source model. 00:59.180 --> 01:01.970 This is something that is so valuable. 01:01.970 --> 01:06.380 Generating data sets is something that you need for many different purposes. 01:06.380 --> 01:14.600 And I want to give you a very, um, wide remit to decide how you want to go about doing this, but 01:14.600 --> 01:21.590 I'm looking for something where you can describe a kind of data set you want, and maybe it's descriptions 01:21.590 --> 01:29.420 of products, maybe it's descriptions of, uh, um, uh, job postings, whatever it is you want to 01:29.450 --> 01:36.260 be able to, to tell your product what it is, what kind of data that you're working with and let it 01:36.260 --> 01:45.080 dream up, uh, diverse outputs, diverse test set that you'll be able to use when experimenting with 01:45.080 --> 01:47.300 your business area in the future. 01:47.300 --> 01:55.610 So this synthetic data generator is going to be a valuable tool for yourself, for me and for for this, 01:55.640 --> 01:59.090 both for this course and for future business problems you tackle. 01:59.090 --> 02:03.890 So it's worth investing some time in, and it's worth giving it a gradio UI while you're doing it. 02:03.920 --> 02:05.720 And that's going to be the super easy part. 02:05.720 --> 02:08.420 So I have a shot at that. 02:08.450 --> 02:11.060 It will apply to your business area no matter what you do. 02:11.060 --> 02:13.790 It's going to be useful and you're going to really enjoy it. 02:16.160 --> 02:25.250 And then that would then complete week three, wrapping up your third week of your journey towards being 02:25.250 --> 02:27.800 a proficient LM engineer. 02:27.830 --> 02:31.790 You can already, of course, code confidently with frontier models. 02:31.790 --> 02:34.940 You must be sick of me saying that now because you're that good. 02:34.940 --> 02:37.100 You can build an AI assistant. 02:37.100 --> 02:39.980 You can have it be multimodal, you can have it use tools. 02:39.980 --> 02:46.010 You can have it be consist of multiple smaller agents that carry out specialist tasks. 02:46.040 --> 02:52.460 And of course, at this point you can create an LM solution that combines calls to frontier models. 02:52.460 --> 02:54.680 And it can call open source models. 02:54.680 --> 03:03.080 And you can use the pipeline API, using it to to carry out a large variety of common inference tasks. 03:03.080 --> 03:10.550 And you can also use the lower level hugging face APIs, the Tokenizers, and the models for inference 03:10.550 --> 03:11.780 tasks. 03:12.470 --> 03:18.980 So congratulations once again, you should be very proud next week. 03:19.010 --> 03:20.990 Next week we change topics. 03:20.990 --> 03:23.240 There's a thorny question. 03:23.240 --> 03:24.980 It's a question I get asked all the time. 03:24.980 --> 03:31.010 It's something which is, uh, where there's actually a lot of great resources to help. 03:31.010 --> 03:37.440 It's about how do you pick the right model for the for a given task that you have to work on. 03:37.440 --> 03:39.870 There are so many models, there are so many options. 03:39.870 --> 03:41.220 There's for staff. 03:41.250 --> 03:43.290 There's there's do you go closed source or open source? 03:43.290 --> 03:48.000 But then whichever path you take, there are so many possibilities. 03:48.000 --> 03:52.830 And how do you navigate through this to decide which one is right for a particular problem. 03:52.830 --> 03:53.850 And that is the key. 03:53.880 --> 03:55.470 It depends on the problem. 03:55.470 --> 03:58.650 Different models will be appropriate for different problems. 03:58.650 --> 04:00.660 I'm going to show you how to figure that out. 04:00.990 --> 04:02.550 We're going to compare LMS. 04:02.550 --> 04:03.720 We're going to use leaderboards. 04:03.720 --> 04:04.950 We're going to use arenas. 04:04.950 --> 04:08.070 And we're going to do some some work with arenas ourselves. 04:08.070 --> 04:09.360 And that's going to be fun. 04:09.360 --> 04:15.930 And then as our practical work, we're going to to go a different direction than we've gone in the past, 04:16.020 --> 04:21.780 except we did it very briefly once, but we're going to be looking at code generation when we're using 04:21.780 --> 04:29.490 frontier models and open source models to be generating code and tackling some code generation problems. 04:29.490 --> 04:33.060 So that will be a new, interesting perspective for you. 04:33.060 --> 04:38.910 So I'm really excited about next week, and I'm so, so impressed by how much progress you've made already 04:38.910 --> 04:42.990 and how many skills that you've already acquired. 04:43.110 --> 04:49.140 And I will see you for week four for picking the right LLM.