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.