This guide provides a comprehensive walkthrough for deploying an Elasticsearch application on an Azure Virtual Machine using an automated Azure DevOps pipeline. The process is broken down into four main parts: Azure VM Setup, Azure DevOps Pipeline Setup, Troubleshooting and Optimisation, and Security Recommendations.

UV is an excellent alternative to Pyenv, though they serve slightly different purposes. I have been using pyenv for more than 10 years. Is this the time for the alternative? It is important to note that UV doesn't support Python 2.*.

This second part describes how to integrate LangChain with AWS Bedrock to build AI applications. It covers the implementation of AWS Bedrock with Amazon Titan and Claude models, as well as key LangChain components, including prompt templates, embeddings, memory, and chains. Code examples demonstrate everything from basic model invocation to creating conversational agents with memory, perfect for developers building production AI solutions.

Dive into the essentials of Large Language Models (LLMs) and Foundation Models (FMs) on Amazon Web Services (AWS). This guide explores leveraging AWS Bedrock and related services for building and interacting with powerful generative AI models.

Learn about key concepts including prompt engineering, fine-tuning techniques like prompt-based learning and domain adaptation, and managing inference parameters such as Temperature, Top K, and Top P. Discover how to utilise AWS FM APIs. This post provides the foundational knowledge required to get started with LLMS on AWS.