ML 17726 image001

Unleash your Salesforce data using the Amazon Q Salesforce Online connector

Thousands of companies worldwide use Salesforce to manage their sales, marketing, customer service, and other business operations. The Salesforce cloud-based platform centralizes customer information and interactions across the organization, providing sales reps, marketers, and support agents with a unified 360-degree view of each customer. With Salesforce at the heart of their business, companies accumulate vast …

image1 6

Deploy Meta Llama 3.1 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

We’re excited to announce the availability of Meta Llama 3.1 8B and 70B inference support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. Meta Llama 3.1 multilingual large language models (LLMs) are a collection of pre-trained and instruction tuned generative models. Trainium and Inferentia, enabled by the AWS Neuron software development kit …

Multimodal Autoregressive Pre-Training of Large Vision Encoders

*Equal Contributors A dominant paradigm in large multimodal models is to pair a large language de- coder with a vision encoder. While it is well-known how to pre-train and tune language decoders for multimodal tasks, it is less clear how the vision encoder should be pre-trained. A de facto standard is to pre-train the vision …

ML 17032 qlora definition

Accelerating Mixtral MoE fine-tuning on Amazon SageMaker with QLoRA

Companies across various scales and industries are using large language models (LLMs) to develop generative AI applications that provide innovative experiences for customers and employees. However, building or fine-tuning these pre-trained LLMs on extensive datasets demands substantial computational resources and engineering effort. With the increase in sizes of these pre-trained LLMs, the model customization process …

1 UbJVfvN

Boost your Continuous Delivery pipeline with Generative AI

In the domain of software development, AI-driven assistance is emerging as a transformative force to enhance developer experience and productivity and ultimately optimize overall software delivery performance. Many organizations started to leverage AI-based assistants, such as Gemini Code Assist, in developer IDEs to support them in solving more difficult problems, understanding unfamiliar code, generating test …

Instance-Optimal Private Density Estimation in the Wasserstein Distance

Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric for density estimation. For example, when estimating population densities in a geographic region, a small Wasserstein distance means that the estimate is able to capture roughly where the population …

12A1rIZ1ot5WIzmR7JJ89qVAg

Swiss Re & Palantir: Scaling Data Operations with Foundry

Swiss Re & Palantir Scaling Data Operations with Foundry Editor’s note: This guest post is authored by our customer, Swiss Re. Authors Lukasz Lewandowski, Marco Lotz, and Jarek Sobanski lead the core technical team responsible for the implementation of Palantir Foundry at the Swiss reinsurer. They have been managing overall platform operations, core architectural principles, site reliability, …

AudioSegFinal

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). …

Image 1 Test Results WITHOUT BACKOFF AND.max 1000x1000 1

Don’t let resource exhaustion leave your users hanging: A guide to handling 429 errors

Large language models (LLMs) give developers immense power and scalability, but managing resource consumption is key to delivering a smooth user experience. LLMs demand significant computational resources, which means it’s essential to anticipate and handle potential resource exhaustion. If not, you might encounter 429 “resource exhaustion” errors, which can disrupt how users interact with your …