AWS Machine Learning Blog

This post demonstrates how you can bring your existing SageMaker Data Wrangler flows—the instructions created when building data transformations—from SageMaker Studio Classic to SageMaker Canvas. We provide an example of moving files from SageMaker Studio Classic to HAQM Simple Storage Service (HAQM S3) as an intermediate step before importing them into SageMaker Canvas.

While presigned URLs offer a convenient way to grant temporary access to S3 objects, sharing these URLs with people outside of the workteam can lead to unintended access of those objects. To mitigate this risk and enhance the security of SageMaker Ground Truth labeling tasks, we have introduced a new feature that adds an additional layer of security by restricting access to the presigned URLs to the worker’s IP address or virtual private cloud (VPC) endpoint from which they access the labeling task. In this blog post, we show you how to enable this feature, allowing you to enhance your data security as needed, and outline the success criteria for this feature, including the scenarios where it will be most beneficial.

In this post, we discuss an architecture to query structured data using HAQM Q Business, and build out an application to query cost and usage data in HAQM Athena with HAQM Q Business. HAQM Q Business can create SQL queries to your data sources when provided with the database schema, additional metadata describing the columns and tables, and prompting instructions. You can extend this architecture to use additional data sources, query validation, and prompting techniques to cover a wider range of use cases.

The Cohere Rerank 3 Nimble foundation model (FM) is now generally available in HAQM SageMaker JumpStart. This model is the newest FM in Cohere’s Rerank model series, built to enhance enterprise search and Retrieval Augmented Generation (RAG) systems. In this post, we discuss the benefits and capabilities of this new model with some examples. Overview […]

HAQM SageMaker Canvas now empowers enterprises to harness the full potential of their data by enabling support of petabyte-scale datasets. Starting today, you can interactively prepare large datasets, create end-to-end data flows, and invoke automated machine learning (AutoML) experiments on petabytes of data—a substantial leap from the previous 5 GB limit. With over 50 connectors, […]

QnABot on AWS (an AWS Solution) now provides access to HAQM Bedrock foundational models (FMs) and Knowledge Bases for HAQM Bedrock, a fully managed end-to-end Retrieval Augmented Generation (RAG) workflow. You can now provide contextual information from your private data sources that can be used to create rich, contextual, conversational experiences. In this post, we discuss how to use QnABot on AWS to deploy a fully functional chatbot integrated with other AWS services, and delight your customers with human agent like conversational experiences.

HAQM Q Business is a fully managed, generative artificial intelligence (AI)-powered assistant that helps enterprises unlock the value of their data and knowledge. With HAQM Q, you can quickly find answers to questions, generate summaries and content, and complete tasks by using the information and expertise stored across your company’s various data sources and enterprise […]

This post shows how to configure the HAQM Q ServiceNow connector to index your ServiceNow platform and take advantage of generative AI searches in HAQM Q. We use an example of an illustrative ServiceNow platform to discuss technical topics related to AWS services.

In this post, we explore using the Anthropic Claude 3 on HAQM Bedrock large language model (LLM). HAQM Bedrock provides access to several LLMs, such as Anthropic Claude 3, which can be used to generate semi-structured data relevant to the healthcare industry. This can be particularly useful for creating various healthcare-related forms, such as patient intake forms, insurance claim forms, or medical history questionnaires.

For organizations looking beyond the use of out-of-the-box Splunk AI/ML features, this post explores how HAQM SageMaker Canvas, a no-code ML development service, can be used in conjunction with data collected in Splunk to drive actionable insights. We also demonstrate how to use the generative AI capabilities of SageMaker Canvas to speed up your data exploration and help you build better ML models.