Category: Serverless

In this post, we introduce a flexible and scalable solution that simplifies the batch inference workflow. This solution provides a highly scalable approach to managing your FM batch inference needs, such as generating embeddings for millions of documents or running custom evaluation or completion tasks with large datasets.

In this post, we provide a data imputation solution using Amazon SageMaker AI, AWS Lambda, and AWS Step Functions. This solution is designed for environmental analysts, public health officials, and business intelligence professionals who need reliable PM2.5 data for trend analysis, reporting, and decision-making. We sourced our sample training dataset from openAFRICA. Our solution predicts PM2.5 values using time-series forecasting.

In this post, we explore an innovative solution that uses Amazon Bedrock Agents, powered by Amazon Nova Lite, to create a conversational interface for Athena queries. We use AWS Cost and Usage Reports (AWS CUR) as an example, but this solution can be adapted for other databases you query using Athena. This approach democratizes data access while preserving the powerful analytical capabilities of Athena, so you can interact with your data using natural language.

In this post, we show how you can build an automated, serverless solution to transform webinar recordings into comprehensive handouts using Amazon Bedrock Data Automation for video analysis. We walk you through the implementation of Amazon Bedrock Data Automation to transcribe and detect slide changes, as well as the use of Amazon Bedrock foundation models (FMs) for transcription refinement, combined with custom AWS Lambda functions orchestrated by AWS Step Functions.

PerformLine operates within the marketing compliance industry, a specialized subset of the broader compliance software market, which includes various compliance solutions like anti-money laundering (AML), know your customer (KYC), and others. In this post, PerformLine and AWS explore how PerformLine used Amazon Bedrock to accelerate compliance processes, generate actionable insights, and provide contextual data—delivering the speed and accuracy essential for large-scale oversight.

In this blog post, we showcase a powerful solution that seamlessly integrates AWS generative AI capabilities in the form of large language models (LLMs) based on Amazon Bedrock into the Office experience. By harnessing the latest advancements in generative AI, we empower employees to unlock new levels of efficiency and creativity within the tools they already use every day.

In this post, we explore what language embeddings are and how they can be used to enhance your application. We show how, by using the properties of embeddings, we can implement a real-time zero-shot classifier and can add powerful features such as semantic search.

In this post, we show you an innovative solution to a challenge faced by security teams in highly regulated industries: the efficient security analysis of vast amounts of video recordings from Privileged Access Management (PAM) systems. We demonstrate how you can use Anthropic’s Claude 3 family of models and Amazon Bedrock to perform the complex task of analyzing video recordings of server console sessions and perform queries to highlight any potential security anomalies.

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. In this post, I’ll show you how to use Amazon Bedrock—with its fully managed, on-demand API—with your Amazon SageMaker trained or fine-tuned model.

This post discusses how to use AWS Step Functions to efficiently coordinate multi-step generative AI workflows, such as parallelizing API calls to Amazon Bedrock to quickly gather answers to lists of submitted questions. We also touch on the usage of Retrieval Augmented Generation (RAG) to optimize outputs and provide an extra layer of precision, as well as other possible integrations through Step Functions.