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WEBVTT
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Now, look, I know that I went through that very fast, but maybe, uh, you're still, uh, blinking
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at the end of that.
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But that's because the point is that you should go back now and do this yourself and see it.
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And as you run that code and you see what's going on in the modal screens, I think it's going to make
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complete sense.
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Um, in case you have any problems with that, uh, hugging face token, I'm going to put better instructions
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in the Jupyter lab.
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So that's very clear for you.
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And yeah, if I think you'll find that it will, that it will be fairly straightforward and you'll see
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how that works and why it's working so fast.
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There's the first time you run it for a while.
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There's several minutes for it to warm up.
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But then subsequently, because we've cached the model weights and we've loaded it into memory, it
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should run quickly as it did just then.
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So with that, you have now learned how to take a model and how to deploy it to production so that people
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could call it just with Python code for production purposes within applications outside something like
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a Jupyter Lab.
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And hopefully you're now beginning to appreciate that.
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We do have a big week, and it is an epic project, and there's a lot to be done.
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In fact, uh, the next day's worth of activities is the biggest of the lot.
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There's an awful lot happening.
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Um, but just to remind yourself for today that that this was about deploying models in production using
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modal, the serverless platform.
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In some ways, it's similar to when we deployed a model or I deployed a model to the hugging face using
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hugging face endpoints.
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Um, but you can see the the extra functionality that you get with this, the ability to configure infrastructure
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with code and the way that the pricing works, it's a very, very powerful platform.
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Next time you'll be able to build an advanced Wragg solution, you're saying I already you already got
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Wragg.
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We've done Wragg.
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We know Wragg well.
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You're going to know it even more.
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Next time.
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We're going to use Wragg.
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We're going to do it directly without chain.
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We're pros now.
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We don't need long chain.
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We can do it ourselves.
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We're going to look things up in a Chrome data store and use it to give context to a model, but it's
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going to be an enormous great data store.
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And we're going to build something called an ensemble model, which is a kind of model that combines
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the best of multiple models.
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And we're going to be able to deliver production ready code that will span multiple models.
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So it's going to be about, uh, really strengthening your skill set, building expertise as you make
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the transition from being a knowledgeable in LM engineering to being a master of LM engineering.
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And with that, I'll see you next time.