AI

Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. To view this series from the beginning, start with Part 1. This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. The data mesh is a modern approach […]

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Amazon Bedrock Flows is now generally available with enhanced safety and traceability

Today, we are excited to announce the general availability of Amazon Bedrock Flows (previously known as Prompt Flows). With Bedrock Flows, you can quickly build and execute complex generative AI workflows without writing code. Key benefits include: Simplified generative AI workflow development with an intuitive visual interface. Seamless integration of latest foundation models (FMs), Prompts,

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Implement secure API access to your Amazon Q Business applications with IAM federation user access management

Amazon Q Business is a conversational assistant powered by generative AI that enhances workforce productivity by answering questions and completing tasks based on information in your enterprise systems, which each user is authorized to access. AWS recommends using AWS IAM Identity Center when you have a large number of users in order to achieve a

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MIT researchers develop an efficient way to train more reliable AI agents

Fields ranging from robotics to medicine to political science are attempting to train AI systems to make meaningful decisions of all kinds. For example, using an AI system to intelligently control traffic in a congested city could help motorists reach their destinations faster, while improving safety or sustainability. Unfortunately, teaching an AI system to make

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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).

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