AWS Machine Learning Blog

Category: Generative AI

AI21 Labs Jamba-Instruct model is now available in Amazon Bedrock

We are excited to announce the availability of the Jamba-Instruct large language model (LLM) in Amazon Bedrock. Jamba-Instruct is built by AI21 Labs, and most notably supports a 256,000-token context window, making it especially useful for processing large documents and complex Retrieval Augmented Generation (RAG) applications. What is Jamba-Instruct Jamba-Instruct is an instruction-tuned version of […]

How Krikey AI harnessed the power of Amazon SageMaker Ground Truth to accelerate generative AI development

This post is co-written with Jhanvi Shriram and Ketaki Shriram from Krikey. Krikey AI is revolutionizing the world of 3D animation with their innovative platform that allows anyone to generate high-quality 3D animations using just text or video inputs, without needing any prior animation experience. At the core of Krikey AI’s offering is their powerful […]

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 […]

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 […]

Streamline financial workflows with generative AI for email automation

This post explains a generative artificial intelligence (AI) technique to extract insights from business emails and attachments. It examines how AI can optimize financial workflow processes by automatically summarizing documents, extracting data, and categorizing information from email attachments. This enables companies to serve more clients, direct employees to higher-value tasks, speed up processes, lower expenses, enhance data accuracy, and increase efficiency.