AWS Machine Learning Blog

Category: Generative AI

Advanced RAG patterns on Amazon SageMaker

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 […]

Efficient continual pre-training LLMs for financial domains

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 […]

Achieve DevOps maturity with BMC AMI zAdviser Enterprise and Amazon Bedrock

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.

Unlock the potential of generative AI in industrial operations

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.

Enhance performance of generative language models with self-consistency prompting on Amazon Bedrock

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.

Transform one-on-one customer interactions: Build speech-capable order processing agents with AWS and generative AI

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, […]

The journey of PGA TOUR’s generative AI virtual assistant, from concept to development to prototype

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 […]

Enhance code review and approval efficiency with generative AI using Amazon Bedrock

In the world of software development, code review and approval are important processes for ensuring the quality, security, and functionality of the software being developed. However, managers tasked with overseeing these critical processes often face numerous challenges, such as the following: Lack of technical expertise – Managers may not have an in-depth technical understanding of […]

Best practices to build generative AI applications on AWS

Generative AI applications driven by foundational models (FMs) are enabling organizations with significant business value in customer experience, productivity, process optimization, and innovations. However, adoption of these FMs involves addressing some key challenges, including quality output, data privacy, security, integration with organization data, cost, and skills to deliver. In this post, we explore different approaches […]

Automate the process to change image backgrounds using Amazon Bedrock and AWS Step Functions

Many customers, including those in creative advertising, media and entertainment, ecommerce, and fashion, often need to change the background in a large number of images. Typically, this involves manually editing each image with photo software. This can take a lot of effort, especially for large batches of images. However, Amazon Bedrock and AWS Step Functions […]