AWS Machine Learning Blog

Use Llama 3.1 405B for synthetic data generation and distillation to fine-tune smaller models

Today, we are excited to announce the availability of the Llama 3.1 405B model on Amazon SageMaker JumpStart, and Amazon Bedrock in preview. The Llama 3.1 models are a collection of state-of-the-art pre-trained and instruct fine-tuned generative artificial intelligence (AI) models in 8B, 70B, and 405B sizes. Amazon SageMaker JumpStart is a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. Amazon Bedrock offers a straightforward way to build and scale generative AI applications with Meta Llama models, using a single API.

Llama 3.1 models are now available in Amazon SageMaker JumpStart

Today, we are excited to announce that the state-of-the-art Llama 3.1 collection of multilingual large language models (LLMs), which includes pre-trained and instruction tuned generative AI models in 8B, 70B, and 405B sizes, is available through Amazon SageMaker JumpStart to deploy for inference. Llama is a publicly accessible LLM designed for developers, researchers, and businesses to build, experiment, and responsibly scale their generative artificial intelligence (AI) ideas. In this post, we walk through how to discover and deploy Llama 3.1 models using SageMaker JumpStart.

Metadata filtering for tabular data with Knowledge Bases for Amazon Bedrock

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API. To equip FMs with up-to-date and proprietary information, organizations use Retrieval Augmented Generation (RAG), a technique that […]

Retrieval Workflow

Secure AccountantAI Chatbot: Lili’s journey with Amazon Bedrock

This post was written in collaboration with Liran Zelkha and Eyal Solnik from Lili. Small business proprietors tend to prioritize the operational aspects of their enterprises over administrative tasks, such as maintaining financial records and accounting. While hiring a professional accountant can provide valuable guidance and expertise, it can be cost-prohibitive for many small businesses. […]

How Mend.io unlocked hidden patterns in CVE data with Anthropic Claude on Amazon Bedrock

This post is co-written with Maciej Mensfeld from Mend.io. In the ever-evolving landscape of cybersecurity, the ability to effectively analyze and categorize Common Vulnerabilities and Exposures (CVEs) is crucial. This post explores how Mend.io, a cybersecurity firm, used Anthropic Claude on Amazon Bedrock to classify and identify CVEs containing specific attack requirements details. By using […]

How Deloitte Italy built a digital payments fraud detection solution using quantum machine learning and Amazon Braket

As digital commerce expands, fraud detection has become critical in protecting businesses and consumers engaging in online transactions. Implementing machine learning (ML) algorithms enables real-time analysis of high-volume transactional data to rapidly identify fraudulent activity. This advanced capability helps mitigate financial risks and safeguard customer privacy within expanding digital markets. Deloitte is a strategic global […]

Amazon SageMaker unveils the Cohere Command R fine-tuning model

AWS announced the availability of the Cohere Command R fine-tuning model on Amazon SageMaker. This latest addition to the SageMaker suite of machine learning (ML) capabilities empowers enterprises to harness the power of large language models (LLMs) and unlock their full potential for a wide range of applications. Cohere Command R is a scalable, frontier […]

Overview of the QSI solution

Derive meaningful and actionable operational insights from AWS Using Amazon Q Business

As a customer, you rely on Amazon Web Services (AWS) expertise to be available and understand your specific environment and operations. Today, you might implement manual processes to summarize lessons learned, obtain recommendations, or expedite the resolution of an incident. This can be time consuming, inconsistent, and not readily accessible. This post shows how to […]

Accelerate your generative AI distributed training workloads with the NVIDIA NeMo Framework on Amazon EKS

In today’s rapidly evolving landscape of artificial intelligence (AI), training large language models (LLMs) poses significant challenges. These models often require enormous computational resources and sophisticated infrastructure to handle the vast amounts of data and complex algorithms involved. Without a structured framework, the process can become prohibitively time-consuming, costly, and complex. Enterprises struggle with managing […]