Large language models (LLMs) have achieved remarkable success in various natural language processing (NLP) tasks, but they may not always generalize well to specific domains or tasks. You may need to customize an LLM to adapt to your unique use case, improving its performance on your specific dataset or task.Continue Reading
This post is a joint collaboration between Salesforce and AWS and is being cross-published on both the Salesforce Engineering Blog and the AWS Machine Learning Blog. Salesforce, Inc. is an American cloud-based software company headquartered in San Francisco, California. It provides customer relationship management (CRM) software and applications focused onContinue Reading
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 designedContinue Reading
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 CommandContinue Reading
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 followingContinue Reading
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 undifferentiatedContinue Reading
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 codingContinue Reading
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, applyContinue Reading
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 varyContinue Reading
This blog post is co-written with Qaish Kanchwala from The Weather Company. As industries begin adopting processes dependent on machine learning (ML) technologies, it is critical to establish machine learning operations (MLOps) that scale to support growth and utilization of this technology. MLOps practitioners have many options to establish anContinue Reading
