AWS Machine Learning Blog

This post offers insights for businesses aiming to use artificial intelligence (AI) and cloud technologies to enhance customer service and streamline operations. We share how Rocket Mortgage’s use of AWS services set a new industry standard and demonstrate how to apply these principles to transform your client interactions and processes.

In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.

In this blog post, we explore how you can use Amazon Q Business to build generative AI assistants that enhance employee experience and boost productivity. Amazon Q Business seamlessly integrates with internal data sources, knowledge bases, and productivity tools to equip your workforce with instant access to information, automated tasks, and personalized support.

In this post, we show you how the new document-level report in Amazon Kendra provides enhanced visibility and observability into the document processing lifecycle during data source sync jobs.

In this post, AWS collaborates with Meta’s PyTorch team to showcase how you can use PyTorch’s torchtune library to fine-tune Meta Llama-like architectures while using a fully-managed environment provided by Amazon SageMaker Training.

In this post, we show you how to configure Amazon Bedrock Knowledge Bases with SharePoint Online as a data source. By connecting SharePoint Online as a data source, employees can interact with the organization’s knowledge and data stored in SharePoint using natural language, making it straightforward to find relevant information, extract key points, and derive valuable insights.

In this post, we walk through how AWS can help accelerate a brand’s creative efforts with access to a powerful image-to-image model from Stable Diffusion available on Amazon Bedrock to interactively create and edit art and logo images.

In this post, we present an automated solution to provide a consistent and responsible personalization experience for your customers by using smaller LLMs for website personalization tailored to businesses and industries. This decomposes the complex task into subtasks handled by task / domain adopted LLMs, adhering to company guidelines and human expertise.

In this post, we present to you an in-depth guide to starting a continual pre-training job using PyTorch Fully Sharded Data Parallel (FSDP) for Mistral AI’s Mathstral model with SageMaker HyperPod.

In this post, we show you how Zeta Global, a data-driven marketing technology company, has built an efficient MLOps platform to streamline the end-to-end ML workflow, from data ingestion to model deployment, while optimizing resource utilization and cost efficiency.