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 […]

Manage Amazon SageMaker JumpStart foundation model access with private hubs

Amazon SageMaker JumpStart is a machine learning (ML) hub offering pre-trained models and pre-built solutions. It provides access to hundreds of foundation models (FMs). A private hub is a feature in SageMaker JumpStart that allows an organization to share their models and notebooks so as to centralize model artifacts, facilitate discoverability, and increase the reuse […]

eSentire delivers private and secure generative AI interactions to customers with Amazon SageMaker

eSentire is an industry-leading provider of Managed Detection & Response (MDR) services protecting users, data, and applications of over 2,000 organizations globally across more than 35 industries. These security services help their customers anticipate, withstand, and recover from sophisticated cyber threats, prevent disruption from malicious attacks, and improve their security posture. In 2023, eSentire was […]

Question to answer flow example

Imperva optimizes SQL generation from natural language using Amazon Bedrock

This is a guest post co-written with Ori Nakar from Imperva. Imperva Cloud WAF protects hundreds of thousands of websites against cyber threats and blocks billions of security events every day. Counters and insights based on security events are calculated daily and used by users from multiple departments. Millions of counters are added daily, together […]

Create natural conversations with Amazon Lex QnAIntent and Knowledge Bases for Amazon Bedrock

Customer service organizations today face an immense opportunity. As customer expectations grow, brands have a chance to creatively apply new innovations to transform the customer experience. Although meeting rising customer demands poses challenges, the latest breakthroughs in conversational artificial intelligence (AI) empowers companies to meet these expectations. Customers today expect timely responses to their questions […]

Evaluate the reliability of Retrieval Augmented Generation applications using Amazon Bedrock

In this post, we show you how to evaluate the performance, trustworthiness, and potential biases of your RAG pipelines and applications on Amazon Bedrock. 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.

Connect to Amazon services using AWS PrivateLink in Amazon SageMaker

In this post, we present a solution for configuring SageMaker notebook instances to connect to Amazon Bedrock and other AWS services with the use of AWS PrivateLink and Amazon Elastic Compute Cloud (Amazon EC2) security groups.

Maximize your Amazon Translate architecture using strategic caching layers

In this post, we explain how setting up a cache for frequently accessed translations can benefit organizations that need scalable, multi-language translation across large volumes of content. You’ll learn how to build a simple caching mechanism for Amazon Translate to accelerate turnaround times.

Deploy a Slack gateway for Amazon Bedrock

In today’s fast-paced digital world, streamlining workflows and boosting productivity are paramount. That’s why we’re thrilled to share an exciting integration that will take your team’s collaboration to new heights. Get ready to unlock the power of generative artificial intelligence (AI) and bring it directly into your Slack workspace. Imagine the possibilities: Quick and efficient […]

Improving air quality with generative AI

This post presents a solution that uses a generative artificial intelligence (AI) to standardize air quality data from low-cost sensors in Africa, specifically addressing the air quality data integration problem of low-cost sensors. The solution harnesses the capabilities of generative AI, specifically Large Language Models (LLMs), to address the challenges posed by diverse sensor data and automatically generate Python functions based on various data formats. The fundamental objective is to build a manufacturer-agnostic database, leveraging generative AI’s ability to standardize sensor outputs, synchronize data, and facilitate precise corrections.

Use zero-shot large language models on Amazon Bedrock for custom named entity recognition

Name entity recognition (NER) is the process of extracting information of interest, called entities, from structured or unstructured text. Manually identifying all mentions of specific types of information in documents is extremely time-consuming and labor-intensive. Some examples include extracting players and positions in an NFL game summary, products mentioned in an AWS keynote transcript, or […]