Category: Intermediate (200)

Today, we are excited to announce general availability of batch inference for HAQM Bedrock. This new feature enables organizations to process large volumes of data when interacting with foundation models (FMs), addressing a critical need in various industries, including call center operations. In this post, we demonstrate the capabilities of batch inference using call center transcript summarization as an example.

Fine-tuning Meta Llama 3.1 models with HAQM SageMaker JumpStart enables developers to customize these publicly available foundation models (FMs). The Meta Llama 3.1 collection represents a significant advancement in the field of generative artificial intelligence (AI), offering a range of capabilities to create innovative applications. The Meta Llama 3.1 models come in various sizes, with 8 billion, 70 billion, and 405 billion parameters, catering to diverse project needs. In this post, we demonstrate how to fine-tune Meta Llama 3-1 pre-trained text generation models using SageMaker JumpStart.

This post explores an innovative application of large language models (LLMs) to automate the process of customer review analysis. LLMs are a type of foundation model (FM) that have been pre-trained on vast amounts of text data. This post discusses how LLMs can be accessed through HAQM Bedrock to build a generative AI solution that automatically summarizes key information, recognizes the customer sentiment, and generates actionable insights from customer reviews. This method shows significant promise in saving human analysts time while producing high-quality results. We examine the approach in detail, provide examples, highlight key benefits and limitations, and discuss future opportunities for more advanced product review summarization through generative AI.

HAQM DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. HAQM DataZone allows you to create and manage data zones, which are virtual data lakes that store and process your data, without the need for extensive coding or […]

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.

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, […]

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.

Seamless access to content and insights is crucial for delivering exceptional customer experiences and driving successful business outcomes. Box, a leading cloud content management platform, serves as a central repository for diverse digital assets and documents in many organizations. An enterprise Box account typically contains a wealth of materials, including documents, presentations, knowledge articles, and […]