aws

Unified CloudOps platform with AI-SRE, AI-FinOps, AI-K8sOps, and the Agentic Automation Builder without fragmented tools, context switching, or model lock-in.

A work in progress to build out solutions in Rust for MLOPs

Build LangChain Applications on AWS

Conteúdo em PT-BR para a AWS AI Practitioner (AIF-C01), com foco em fundamentos de IA/ML na AWS, serviços gerenciados (Bedrock, Sagemaker, etc.), boas práticas de governança e aplicações de negócio.

RAG with langchain using Amazon Bedrock and Amazon OpenSearch

OCR, Archive, Index and Search: Implementation agnostic OCR framework.

:zap: Cloud-native, AI-powered, document processing pipelines on AWS.

This repo provides sample generative AI stacks built atop the AWS Generative AI CDK Constructs.

A scalable RAG platform combining LangGraph agents, hybrid retrieval (Vector+Graph), and Ray orchestration on Kubernetes.

This repository is intended for those looking to dive deep on advanced Text-to-SQL concepts.

書籍「Amazon Bedrock 生成AIアプリ開発入門」のサンプルコード

A multimodal chat interface with many tools.

A demo ChatBot application developed using Amazon Bedrock service's KnowledgeBase, Agent and other AWS's serveless GenAI solution.

A collection of personally developed projects contributing towards the advancement of Artificial General Intelligence(AGI)

Run any AWS Lambda function as a Large Language Model (LLM) tool without code changes using Anthropic's Model Context Protocol (MCP).

Prototyping Generative AI Use Cases with Amazon Bedrock and Langchain

Seamlessly invoke Amazon Bedrock or your custom models, enabling a smooth experience with AWS GenAI services.

Opinionated sample on how to build/deploy a RAG web app on AWS powered by Amazon Bedrock and PGVector (on Amazon RDS)

Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.

A comprehensive toolkit for deploying production-ready Generative AI infrastructure on Amazon EKS. Includes pre-configured components for: 🚀 AI Gateway (LiteLLM) 🤖 LLM Serving (vLLM, SGLang, Ollama) 📊 Vector Databases, 🔍 Embedding Models (TEI) 📈 Observability (Langfuse, Phoenix) etc. Fast-track your GenAI deployment with Kubernetes