Category: Generative AI

This is a guest post co-written with Michael Feil at Gradient. Evaluating the performance of large language models (LLMs) is an important step of the pre-training and fine-tuning process before deployment. The faster and more frequent you’re able to validate performance, the higher the chances you’ll be able to improve the performance of the model. […]

This blog post is co-written with Hwalsuk Lee at Upstage. Today, we’re excited to announce that the Solar foundation model developed by Upstage is now available for customers using Amazon SageMaker JumpStart. Solar is a large language model (LLM) 100% pre-trained with Amazon SageMaker that outperforms and uses its compact size and powerful track records […]

This is a guest post co-written with Meta’s PyTorch team and is a continuation of Part 1 of this series, where we demonstrate the performance and ease of running PyTorch 2.0 on AWS. Machine learning (ML) research has proven that large language models (LLMs) trained with significantly large datasets result in better model quality. In […]

Today, customers of all industries—whether it’s financial services, healthcare and life sciences, travel and hospitality, media and entertainment, telecommunications, software as a service (SaaS), and even proprietary model providers—are using large language models (LLMs) to build applications like question and answering (QnA) chatbots, search engines, and knowledge bases. These generative AI applications are not only […]

Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. For example, Meta’s Llama models are trained on datasets such as CommonCrawl, C4, Wikipedia, and ArXiv. These datasets encompass a broad range of topics and domains. Although the resulting models yield amazingly good results for general tasks, such as […]

This blog post discusses how BMC Software added AWS Generative AI capabilities to its product BMC AMI zAdviser Enterprise. The zAdviser uses Amazon Bedrock to provide summarization, analysis, and recommendations for improvement based on the DORA metrics data.

In this post, multi-shot prompts are retrieved from an embedding containing successful Python code run on a similar data type (for example, high-resolution time series data from Internet of Things devices). The dynamically constructed multi-shot prompt provides the most relevant context to the FM, and boosts the FM’s capability in advanced math calculation, time series data processing, and data acronym understanding. This improved response facilitates enterprise workers and operational teams in engaging with data, deriving insights without requiring extensive data science skills.

With the batch inference API, you can use Amazon Bedrock to run inference with foundation models in batches and get responses more efficiently. This post shows how to implement self-consistency prompting via batch inference on Amazon Bedrock to enhance model performance on arithmetic and multiple-choice reasoning tasks.

In today’s landscape of one-on-one customer interactions for placing orders, the prevailing practice continues to rely on human attendants, even in settings like drive-thru coffee shops and fast-food establishments. This traditional approach poses several challenges: it heavily depends on manual processes, struggles to efficiently scale with increasing customer demands, introduces the potential for human errors, […]

This is a guest post co-written with Scott Gutterman from the PGA TOUR. Generative artificial intelligence (generative AI) has enabled new possibilities for building intelligent systems. Recent improvements in Generative AI based large language models (LLMs) have enabled their use in a variety of applications surrounding information retrieval. Given the data sources, LLMs provided tools […]