Using Amazon Rekognition to improve bicycle safety
To better protect themselves, many cyclists are starting to ride with cameras mounted to the front or back of their bicycle. In this blog post, I will demonstrate a machine…
Advanced (300)
Develop a RAG-based application using Amazon Aurora with Amazon Kendra
RAG retrieves data from a preexisting knowledge base (your data), combines it with the LLM’s knowledge, and generates responses with more human-like language. However, in order for generative AI to…
HCLTech’s AWS powered AutoWise Companion: A seamless experience for informed automotive buyer decisions with data-driven design
This post introduces HCLTech’s AutoWise Companion, a transformative generative AI solution designed to enhance customers’ vehicle purchasing journey. In this post, we analyze the current industry challenges and guide readers…
Search enterprise data assets using LLMs backed by knowledge graphs
In this post, we present a generative AI-powered semantic search solution that empowers business users to quickly and accurately find relevant data assets across various enterprise data sources. In this…
Accelerating Mixtral MoE fine-tuning on Amazon SageMaker with QLoRA
In this post, we demonstrate how you can address the challenges of model customization being complex, time-consuming, and often expensive by using fully managed environment with Amazon SageMaker Training jobs…
Enhance speech synthesis and video generation models with RLHF using audio and video segmentation in Amazon SageMaker
In this post, we show you how to implement an audio and video segmentation solution using SageMaker Ground Truth. We guide you through deploying the necessary infrastructure using AWS CloudFormation,…
From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2
This post focuses on doing RAG on heterogeneous data formats. We first introduce routers, and how they can help managing diverse data sources. We then give tips on how to…
Fine-tune Meta Llama 3.2 text generation models for generative AI inference using Amazon SageMaker JumpStart
In this post, we demonstrate how to fine-tune Meta’s latest Llama 3.2 text generation models, Llama 3.2 1B and 3B, using Amazon SageMaker JumpStart for domain-specific applications. By using the…
Create a generative AI–powered custom Google Chat application using Amazon Bedrock
AWS offers powerful generative AI services, including Amazon Bedrock, which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in…
Fine-tune Meta Llama 3.1 models using torchtune on Amazon SageMaker
In this post, AWS collaborates with Meta’s PyTorch team to showcase how you can use Meta’s torchtune library to fine-tune Meta Llama-like architectures while using a fully-managed environment provided by…