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WEBVTT
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Well.
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Hi there.
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So you've made it to day two of week eight, and I am super grateful that you've been hanging on, and
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that you're now at the point where there are four more days to go.
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And let me tell you something this day, this is probably the toughest day of the lot of the entire
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course.
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So if you survive through this day and you're on top of it and you're getting it and you're running
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the code and it's all making sense, then you you're already basically mastering LM engineering.
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This is what it's about.
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Today is going to be tough.
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Be prepared.
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But as it happens it's going to be really interesting.
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And we're going to get some great achievements done.
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So four days to go of the eight week journey to LM mastery.
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We're in the home stretch.
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You can already, of course generate text and code with frontier models, with open source, with hugging
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face back to front, you can follow a strategy to solve problems with curating data sets, which is
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hard making baseline models and fine tuning frontier models, and then carrying out a full end to end
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process to fine tune open source LMS.
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And finally, what we did last time is taking our fine tuned LMS and deploying it to production.
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We use modal.
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There are many others, but modal is great, and we use it in such a way that you can just write a simple
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piece of Python code that can run locally.
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Apparently it looks like it's running locally, but it in fact it is calling out to your model running
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on the cloud in modal.
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Paying only for what you use.
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So today we take a side step to look at more pricing models as we build our final final pricing model.
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That will be what's called an ensemble, a combination of multiple models.
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We're going to go back to Rag, and we're going to use Rag to build a frontier model solution that will
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operate against a massive Chrome data store.
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And to make things different to last time, we're not going to use Lang chain.
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We're just going to directly look up in the vector store and use that to build our prompt ourselves,
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which is good.
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Good to understand exactly how it works under the covers.
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We're going to then build this ensemble model with a high level of expertise and be able to deliver
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production ready code, calling a number of models.
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And I think perhaps the most important thing to say here is that whilst a lot of what we'll be doing
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is something of a repeat of things we've done before, just in a bit more of an industrial strength
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way, this is about solidifying what you've learned.
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This is about going from the point of being fairly confident with different aspects of LM engineering
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into being advanced, and so do take the time to go through the code and take this as an opportunity
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to practice, practice, practice, and get to the point of feeling like you've built true expertise.
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All right.
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So quick, a reminder on what is the project we're working on here.
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Project called The Price is Right building this autonomous Agentic framework agent workflows that will
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watch for when deals get published online, estimate their prices and send a push notification if it
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looks like this is a bargain and we are going to have these different agents collaborating to solve
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the problem, including our frontier busting model, our fine tuned model that significantly outperformed
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GPT four and Claude.
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All right, so you may remember last time I showed you this diagram that showed our agent workflows,
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the user interface on the left, the framework that we'll be using, planning agents to coordinate activities.
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And then the three the scanning agent looking for promising deals, ensemble estimating prices and a
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messaging agent that will send us push notifications.
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So I wanted to mention that this was a bit of a simplification, because the ensemble agent itself will
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be calling out to three different models.
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It's going to be calling a frontier agent, which is going to be a Rag workflow to price products based
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on an inventory of lots of existing products that it can look up.
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It's a perfect use case for rag.
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Of course, you may have already thought of it as I say, so you may have already been wondering why
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we didn't even try this.
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Well, we're going to.
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Today, the ensemble agent will use the specialist agent that we already have and built and have like
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a specialist agent class to represent, and it's going to use a random forest agent, which will be
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an agent using a random forest traditional machine learning approach.
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Except there's going to be a twist.
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It's going to be using vector embeddings that will use a transformer architecture.
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So it is a sort of modern take on traditional ML.
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So basically today we are going to be working on all of the icons there in red.
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We're going to be working on the ensemble agent.
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We've already built the specialist agent.
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So the other two will be part of our conquest today.
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And we will end today with an ensemble agent that is able to calculate prices, drawing on multiple
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other estimators.
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That's the challenge.
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Let's get over to JupyterLab.