GenAI on K8s: Building with Amazon Bedrock
We have spent this whole series talking about “Big Data”—Spark, Kafka, and SQL engines. But the hottest topic in tech right now isn’t just data processing; it’s Generative AI.
In Chapter 11, Neylson Crepalde shows that Kubernetes isn’t just for ETL. It’s also the perfect platform for deploying GenAI applications. By using Amazon Bedrock, we can build intelligent apps without having to manage massive GPU clusters ourselves.
What is Amazon Bedrock?
Think of Amazon Bedrock as a “Model as a Service.” Instead of downloading a massive 70B parameter model and trying to run it on expensive servers, you just call an API. Bedrock gives you access to industry-leading foundational models like Claude 3, Llama, and Mistral.
The beauty of this is that you only pay for the tokens you use. No more paying for idle GPUs.
Building the App with Streamlit
The book walks through building a chatbot using Streamlit (a Python library for building data UIs) and LangChain.
The logic is simple:
- Frontend: A clean, interactive chat interface where you can choose between models (like Claude 3 Haiku vs. Sonnet).
- Backend: LangChain handles the conversation history and sends your prompts to the Bedrock runtime API.
- Deployment: We package the whole thing into a Docker container and deploy it to Kubernetes as a standard Deployment.
Why Kubernetes for GenAI?
You might wonder: “Why not just run this on a serverless function?”
The answer is Scale and Reliability. When your GenAI app becomes popular, you need to handle hundreds of concurrent users. Kubernetes allows you to:
- Auto-scale: Spin up more replicas of your frontend as traffic grows.
- Manage Secrets: Safely store your AWS credentials using Kubernetes Secrets.
- Unified Platform: Your AI app lives right next to the data pipeline that feeds it.
Retrieval-Augmented Generation (RAG)
The book doesn’t stop at simple chatbots. It dives into RAG. This is how you stop AI from “hallucinating” (making stuff up). By connecting your GenAI app to a Knowledge Base (like a folder of PDFs on S3), the model can search your actual company data before answering a question.
It turns an “AI that knows everything on the internet” into an “AI that knows everything about YOUR business.”
We’ve come a long way from just running a Docker container. In the next post, we’ll see how to make these models actually execute actions on our behalf.
Next: Action Models with Bedrock Agents Previous: Building an End-to-End Big Data Pipeline - Part 3
Book Details:
- Title: Big Data on Kubernetes: A practical guide to building efficient and scalable data solutions
- Author: Neylson Crepalde
- ISBN: 978-1-83546-214-0