• Deployed Language Model (LLM) powered chatbots in AWS.
• Addressed business and technical requirements for a highly available chatbot solution.
• Explored two architectures: AWS Elastic Kubernetes Service (EKS) and serverless with AWS Lambda.
• Conducted Kubernetes experiments for horizontal pod scaling using Locust.
• Managed SageMaker endpoints using ACK and implemented autoscaling policies through Kubernetes.
• Utilized the Flan-UL2 model deployed as a SageMaker endpoint for enhanced language understanding.
• Integrated the LLM agent with Amazon Lex, enabling its use as a chatbot on websites and AWS Connect.

Ensure you have the following prerequisites before deploying the chatbot:

• AWS account with necessary permissions
• AWS CLI installed and configured
• Kubernetes tools (kubectl & eksctl) for EKS deployment
• Locust for Kubernetes experiments

Follow these steps to get started with deploying LLM powered chatbots in AWS:

1. Clone the repository to your local machine:

   git clone https://github.com/your-username/your-repository.git

2. Review the proposed architectures in the architectures directory and choose the one that suits your requirements.

3. Follow the instructions in the docs directory for detailed documentation on deploying and managing the chatbot.

4. For kubernets experimentation check experiments directory.