From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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220 lines
6.1 KiB
220 lines
6.1 KiB
WEBVTT |
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Well. |
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Fantastic. |
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It's coming up to the end of the week, and that means it's coming up to a challenge for you again, |
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even though I've just given you a challenge to build a Gradio user interface for for what we just saw. |
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But that's an easy challenge. |
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You can do that. |
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No problem. |
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This you need a harder challenge. |
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At the end of the week, it's time for a harder challenge. |
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So the end of week challenge is to build an important business application that we will, in fact, |
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use later in the course. |
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Although yeah, you you won't need to to you won't need to have built it for that because because I'll |
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have done it. |
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But it's really helpful if you've done it. |
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And this is something that you'll be able to use no matter in any business. |
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This, this, this tool will apply to every business vertical and can be useful to you, I guarantee |
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it. |
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And this is what it is. |
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Create your own tool that generates synthetic testing data a test data generator, open source model. |
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This is something that is so valuable. |
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Generating data sets is something that you need for many different purposes. |
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And I want to give you a very, um, wide remit to decide how you want to go about doing this, but |
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I'm looking for something where you can describe a kind of data set you want, and maybe it's descriptions |
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of products, maybe it's descriptions of, uh, um, uh, job postings, whatever it is you want to |
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be able to, to tell your product what it is, what kind of data that you're working with and let it |
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dream up, uh, diverse outputs, diverse test set that you'll be able to use when experimenting with |
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your business area in the future. |
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So this synthetic data generator is going to be a valuable tool for yourself, for me and for for this, |
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both for this course and for future business problems you tackle. |
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So it's worth investing some time in, and it's worth giving it a gradio UI while you're doing it. |
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And that's going to be the super easy part. |
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So I have a shot at that. |
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It will apply to your business area no matter what you do. |
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It's going to be useful and you're going to really enjoy it. |
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And then that would then complete week three, wrapping up your third week of your journey towards being |
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a proficient LM engineer. |
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You can already, of course, code confidently with frontier models. |
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You must be sick of me saying that now because you're that good. |
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You can build an AI assistant. |
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You can have it be multimodal, you can have it use tools. |
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You can have it be consist of multiple smaller agents that carry out specialist tasks. |
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And of course, at this point you can create an LM solution that combines calls to frontier models. |
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And it can call open source models. |
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And you can use the pipeline API, using it to to carry out a large variety of common inference tasks. |
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And you can also use the lower level hugging face APIs, the Tokenizers, and the models for inference |
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tasks. |
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So congratulations once again, you should be very proud next week. |
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Next week we change topics. |
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There's a thorny question. |
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It's a question I get asked all the time. |
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It's something which is, uh, where there's actually a lot of great resources to help. |
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It's about how do you pick the right model for the for a given task that you have to work on. |
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There are so many models, there are so many options. |
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There's for staff. |
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There's there's do you go closed source or open source? |
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But then whichever path you take, there are so many possibilities. |
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And how do you navigate through this to decide which one is right for a particular problem. |
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And that is the key. |
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It depends on the problem. |
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Different models will be appropriate for different problems. |
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I'm going to show you how to figure that out. |
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We're going to compare LMS. |
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We're going to use leaderboards. |
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We're going to use arenas. |
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And we're going to do some some work with arenas ourselves. |
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And that's going to be fun. |
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And then as our practical work, we're going to to go a different direction than we've gone in the past, |
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except we did it very briefly once, but we're going to be looking at code generation when we're using |
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frontier models and open source models to be generating code and tackling some code generation problems. |
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So that will be a new, interesting perspective for you. |
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So I'm really excited about next week, and I'm so, so impressed by how much progress you've made already |
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and how many skills that you've already acquired. |
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And I will see you for week four for picking the right LLM.
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