Create custom images for geospatial analysis with Amazon SageMaker Distribution in Amazon SageMaker Studio

Favorite Amazon SageMaker Studio provides a comprehensive suite of fully managed integrated development environments (IDEs) for machine learning (ML), including JupyterLab, Code Editor (based on Code-OSS), and RStudio. It supports all stages of ML development—from data preparation to deployment, and allows you to launch a preconfigured JupyterLab IDE for efficient

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Shared by AWS Machine Learning July 11, 2024

How BRIA AI used distributed training in Amazon SageMaker to train latent diffusion foundation models for commercial use

Favorite This post is co-written with Bar Fingerman from BRIA AI. This post explains how BRIA AI trained BRIA AI 2.0, a high-resolution (1024×1024) text-to-image diffusion model, on a dataset comprising petabytes of licensed images quickly and economically. Amazon SageMaker training jobs and Amazon SageMaker distributed training libraries took on the

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Shared by AWS Machine Learning July 11, 2024

Improve RAG accuracy with fine-tuned embedding models on Amazon SageMaker

Favorite Retrieval Augmented Generation (RAG) is a popular paradigm that provides additional knowledge to large language models (LLMs) from an external source of data that wasn’t present in their training corpus. RAG provides additional knowledge to the LLM through its input prompt space and its architecture typically consists of the

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Shared by AWS Machine Learning July 11, 2024

Using Agents for Amazon Bedrock to interactively generate infrastructure as code

Favorite In the diverse toolkit available for deploying cloud infrastructure, Agents for Amazon Bedrock offers a practical and innovative option for teams looking to enhance their infrastructure as code (IaC) processes. Agents for Amazon Bedrock automates the prompt engineering and orchestration of user-requested tasks. After being configured, an agent builds

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Shared by AWS Machine Learning July 11, 2024

Achieve up to ~2x higher throughput while reducing costs by ~50% for generative AI inference on Amazon SageMaker with the new inference optimization toolkit – Part 1

Favorite Today, Amazon SageMaker announced a new inference optimization toolkit that helps you reduce the time it takes to optimize generative artificial intelligence (AI) models from months to hours, to achieve best-in-class performance for your use case. With this new capability, you can choose from a menu of optimization techniques,

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Shared by AWS Machine Learning July 10, 2024

Achieve up to ~2x higher throughput while reducing costs by up to ~50% for generative AI inference on Amazon SageMaker with the new inference optimization toolkit – Part 2

Favorite As generative artificial intelligence (AI) inference becomes increasingly critical for businesses, customers are seeking ways to scale their generative AI operations or integrate generative AI models into existing workflows. Model optimization has emerged as a crucial step, allowing organizations to balance cost-effectiveness and responsiveness, improving productivity. However, price-performance requirements

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Shared by AWS Machine Learning July 10, 2024

Fine-tune Anthropic’s Claude 3 Haiku in Amazon Bedrock to boost model accuracy and quality

Favorite Frontier large language models (LLMs) like Anthropic Claude on Amazon Bedrock are trained on vast amounts of data, allowing Anthropic Claude to understand and generate human-like text. Fine-tuning Anthropic Claude 3 Haiku on proprietary datasets can provide optimal performance on specific domains or tasks. The fine-tuning as a deep

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Shared by AWS Machine Learning July 10, 2024

A progress update on our commitment to safe, responsible generative AI

Favorite Responsible AI is a longstanding commitment at Amazon. From the outset, we have prioritized responsible AI innovation by embedding safety, fairness, robustness, security, and privacy into our development processes and educating our employees. We strive to make our customers’ lives better while also establishing and implementing the necessary safeguards

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Shared by AWS Machine Learning July 10, 2024

Empowering everyone with GenAI to rapidly build, customize, and deploy apps securely: Highlights from the AWS New York Summit

Favorite Imagine this—all employees relying on generative artificial intelligence (AI) to get their work done faster, every task becoming less mundane and more innovative, and every application providing a more useful, personal, and engaging experience. To realize this future, organizations need more than a single, powerful large language model (LLM)

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Shared by AWS Machine Learning July 10, 2024

Streamline generative AI development in Amazon Bedrock with Prompt Management and Prompt Flows (preview)

Favorite Today, we’re excited to introduce two powerful new features for Amazon Bedrock: Prompt Management and Prompt Flows, in public preview. These features are designed to accelerate the development, testing, and deployment of generative artificial intelligence (AI) applications, enabling developers and business users to create more efficient and effective solutions

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Shared by AWS Machine Learning July 10, 2024