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In this video, we will continue our MLOps series by highlighting 4 specific patterns for building AI products by connecting to our deployed Amazon SageMaker endpoint. If you’re like me, the question about machine learning once you deploy a model is: “how do we get our model outside of the Juypter Notebook?” Whether you’re deploying the model to Amazon SageMaker, Google CoLab, or some other hosting platform, I have felt that it has been rather confusing or nebulous at best to understand how to get the model “out of the cloud” and into a real application that you or your users could use.
If you’re wondering about this too, then this video is for you!
We are going to go show how to call a deployed model, LLaMA 2 which is a Large Language Model, in a variety of different ways outside of Amazon SageMaker. We will even build our own LLaMA Chat application (a ChatGPT clone) to show you how a real application could connect directly to a model. Here’s what we’ll go through:
(1) 🔊 Build an API with Amazon API Gateway
(2) ☁️ Write a Serverless Function using AWS Lambda
(3) 🐍 Write a Script in Python
(4) 📲 Build an App using Next.js, TypeScript, and Tailwind CSS
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In the last video, we built and deployed our very own LLM (Meta AI’s LLaMA 2) onto Amazon SageMaker and created a real-time endpoint. If you haven’t watched this video yet, check out the 2 hour tutorial here: https://youtu.be/Kq597DYdMEE?si=omcUdLqR2322EnzD
Let’s build some cool tech together! 🚀
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