Steps to Build an AI PoC & 4th of July Celebrations

Dr. Ryan Ries here. This week, I want to walk you through the nitty-gritty of building a generative AI proof of concept (PoC) from scratch on AWS.

Whether you're a startup looking to validate your next big idea or an enterprise testing the waters of AI integration, I think it's important to have a solid guide to get your PoCs up and running.

Before we get started, if you’re in New York or headed to the AWS Summit next week, Mission Cloud is hosting a boat cruise after the Summit. If you’re interested, register here. A little birdy told me we’re launching a new product on the boat too…

Anyways, grab your favorite caffeinated beverage, and let's build a PoC:

1. Set the Stage with AWS Infrastructure

• Create an AWS account if you don’t have one already.
• Fire up the necessary AWS services, like EC2 instances, EKS clusters, or Lambda functions, depending on your deployment strategy.
• Configure AWS Identity and Access Management (IAM) roles and policies for secure access to the required services.

2. Bedrock: Your Data's New Best Friend

Bedrock is an open-source data ingestion and caching framework for LLM applications.

• Follow the installation guide and set up Bedrock on your AWS infrastructure.
• Connect it to your data sources and let it work its magic.

3. OpenSearch: Because Finding Stuff is Quite Important

OpenSearch is an open-source distributed search and analytics suite derived from Elasticsearch.

• Launch an OpenSearch cluster (using Amazon OpenSearch Service or self-managed OpenSearch instances, your call).
• Point it at your Bedrock-ingested data and let the indexing begin.

4. LangChain: The Secret Sauce

LangChain is a framework for building applications with large language models (LLMs).

• Install LangChain and its required dependencies.
• Configure LangChain to use the OpenSearch vector store for semantic search and retrieval.
• Implement your use case (such as question answering, text gen, summarization, etc.) using LangChain’s abstractions.

5. Streamlit: Make It Pretty

Streamlit is a Python library for building interactive data applications.

• Create a new Streamlist application or integrate it into an existing one.
• Build out your user interface (keep it simple; we're just proving concepts here).
• Integrate LangChain and the OpenSearch vector store into your Streamlit application to enable user interactions and queries.

6. Deploy and Test

• Push that Streamlit app to AWS using services like Elastic Beanstalk, ECS, or AWS Lambda functions
• Test your generative AI PoC by interacting with the deployed Streamlit application and evaluating its performance and functionality.

Now, I know what you're thinking – "Ryan, that sounds like a lot of moving parts!"

And you're not wrong.

But here's the beauty of this stack: it's modular, scalable, and built on robust open-source tools. It gives you a solid foundation to experiment and iterate rapidly.

Remember, the goal of a PoC isn't perfection – it's validation

You're testing assumptions, gathering feedback, and paving the way for future development.

So, don't get bogged down in the details. Move fast, break things (in a controlled environment, of course), and learn.

Now, you might be wondering, "What about moving this to production?"

Great question! That's a whole other can of worms involving things like:

• Robust data pipelines and model management.
• Industrial-strength security and compliance measures.
• Comprehensive monitoring and observability.
• Automated deployment and operations.
• And so much more!

But let's not get ahead of ourselves.

Nail that PoC first, and we can talk about production-grade systems another day.

And, as always, my team and I are happy to chat more with you about your PoCs and how we can help you move them to production.

Also, I’m eager to hear what everyone is working on. Have you created a PoC for your business? Any roadblocks you’ve hit? Let me know.

Have a great 4th of July holiday tomorrow!

Until next time,

Ryan

Now, for this week’s prompt & AI-generated image. 

"Generate an image of a robot celebrating the 4th of July holiday. There should be fireworks, hot dogs, and ice cream."

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