AWS Machine Learning Blog

Significant new capabilities make it easier to use Amazon Bedrock to build and scale generative AI applications – and achieve impressive results

We introduced Amazon Bedrock to the world a little over a year ago, delivering an entirely new way to build generative artificial intelligence (AI) applications. With the broadest selection of first- and third-party foundation models (FMs) as well as user-friendly capabilities, Amazon Bedrock is the fastest and easiest way to build and scale secure generative […]

Build a custom UI for Amazon Q Business

Amazon Q is a new generative artificial intelligence (AI)-powered assistant designed for work that can be tailored to your business. Amazon Q can help you get fast, relevant answers to pressing questions, solve problems, generate content, and take actions using the data and expertise found in your company’s information repositories and enterprise systems. When you […]

Scalable intelligent document processing using Amazon Bedrock

In today’s data-driven business landscape, the ability to efficiently extract and process information from a wide range of documents is crucial for informed decision-making and maintaining a competitive edge. However, traditional document processing workflows often involve complex and time-consuming manual tasks, hindering productivity and scalability. In this post, we discuss an approach that uses the […]

Use weather data to improve forecasts with Amazon SageMaker Canvas

Photo by Zbynek Burival on Unsplash Time series forecasting is a specific machine learning (ML) discipline that enables organizations to make informed planning decisions. The main idea is to supply historic data to an ML algorithm that can identify patterns from the past and then use those patterns to estimate likely values about unseen periods […]

Reimagining software development with the Amazon Q Developer Agent

Amazon Q Developer uses generative artificial intelligence (AI) to deliver state-of-the-art accuracy for all developers, taking first place on the leaderboard for SWE-bench, a dataset that tests a system’s ability to automatically resolve GitHub issues. This post describes how to get started with the software development agent, gives an overview of how the agent works, and discusses its performance on public benchmarks. We also delve into the process of getting started with the Amazon Q Developer Agent and give an overview of the underlying mechanisms that make it a state-of-the-art feature development agent.

Get started quickly with AWS Trainium and AWS Inferentia using AWS Neuron DLAMI and AWS Neuron DLC

Starting with the AWS Neuron 2.18 release, you can now launch Neuron DLAMIs (AWS Deep Learning AMIs) and Neuron DLCs (AWS Deep Learning Containers) with the latest released Neuron packages on the same day as the Neuron SDK release. When a Neuron SDK is released, you’ll now be notified of the support for Neuron DLAMIs […]

Sprinklr_Services

Sprinklr improves performance by 20% and reduces cost by 25% for machine learning inference on AWS Graviton3

This is a guest post co-written with Ratnesh Jamidar and Vinayak Trivedi from Sprinklr. Sprinklr’s mission is to unify silos, technology, and teams across large, complex companies. To achieve this, we provide four product suites, Sprinklr Service, Sprinklr Insights, Sprinklr Marketing, and Sprinklr Social, as well as several self-serve offerings. Each of these products are […]

Attack path

How Wiz is empowering organizations to remediate security risks faster with Amazon Bedrock

Wiz is a cloud security platform that enables organizations to secure everything they build and run in the cloud by rapidly identifying and removing critical risks. Over 40% of the Fortune 100 trust Wiz’s purpose-built cloud security platform to gain full-stack visibility, accurate risk prioritization, and enhanced business agility. Organizations can connect Wiz in minutes […]

Code generation using Code Llama 70B and Mixtral 8x7B on Amazon SageMaker

In the ever-evolving landscape of machine learning and artificial intelligence (AI), large language models (LLMs) have emerged as powerful tools for a wide range of natural language processing (NLP) tasks, including code generation. Among these cutting-edge models, Code Llama 70B stands out as a true heavyweight, boasting an impressive 70 billion parameters. Developed by Meta […]

Build RAG applications using Jina Embeddings v2 on Amazon SageMaker JumpStart

Today, we are excited to announce that the Jina Embeddings v2 model, developed by Jina AI, is available for customers through Amazon SageMaker JumpStart to deploy with one click for running model inference. This state-of-the-art model supports an impressive 8,192-tokens context length. You can deploy this model with SageMaker JumpStart, a machine learning (ML) hub […]

Detect email phishing attempts using Amazon Comprehend

Phishing is the process of attempting to acquire sensitive information such as usernames, passwords and credit card details by masquerading as a trustworthy entity using email, telephone or text messages. There are many types of phishing based on the mode of communication and targeted victims. In an Email phishing attempt, an email is sent as […]