Enhance speech synthesis and video generation models with RLHF using audio and video segmentation in Amazon SageMaker

As generative AI models advance in creating multimedia content, the difference between good and great output often lies in the details that only human feedback can capture. Audio and video segmentation provides a structured way to gather this detailed feedback, allowing models to learn through reinforcement learning from human feedback (RLHF) and supervised fine-tuning (SFT). […]

Using responsible AI principles with Amazon Bedrock Batch Inference

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. The […]

Revolutionizing knowledge management: VW’s AI prototype journey with AWS

Today, we’re excited to share the journey of the VW—an innovator in the automotive industry and Europe’s largest car maker—to enhance knowledge management by using generative AI, Amazon Bedrock, and Amazon Kendra to devise a solution based on Retrieval Augmented Generation (RAG) that makes internal information more easily accessible by its users. This solution efficiently […]

Fine-tune large language models with Amazon SageMaker Autopilot

Fine-tuning foundation models (FMs) is a process that involves exposing a pre-trained FM to task-specific data and fine-tuning its parameters. It can then develop a deeper understanding and produce more accurate and relevant outputs for that particular domain. In this post, we show how to use an Amazon SageMaker Autopilot training job with the AutoMLV2 […]