AWS Machine Learning - AI

OCTOBER 31, 2024

AWS offers powerful generative AI services , including Amazon Bedrock , which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. The following figure illustrates the high-level design of the solution.

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AWS Machine Learning - AI

NOVEMBER 7, 2024

Amazon Bedrock Agents coordinates interactions between foundation models (FMs), knowledge bases, and user conversations. The agents also automatically call APIs to perform actions and access knowledge bases to provide additional information. The documents are chunked into smaller segments for more effective processing.

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AWS Machine Learning - AI

NOVEMBER 7, 2024

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. You can use AWS services such as Application Load Balancer to implement this approach. API Gateway also provides a WebSocket API. You also need to consider the operational characteristics and noisy neighbor risks.

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AWS Machine Learning - AI

JULY 3, 2024

Knowledge Bases for Amazon Bedrock is a fully managed capability that helps you securely connect foundation models (FMs) in Amazon Bedrock to your company data using Retrieval Augmented Generation (RAG). In the following sections, we demonstrate how to create a knowledge base with guardrails.

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AWS Machine Learning - AI

AUGUST 5, 2024

An end-to-end RAG solution involves several components, including a knowledge base, a retrieval system, and a generation system. Solution overview The solution provides an automated end-to-end deployment of a RAG workflow using Knowledge Bases for Amazon Bedrock. On the AWS CloudFormation console, create a new stack.

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AWS Machine Learning - AI

OCTOBER 29, 2024

We have built a custom observability solution that Amazon Bedrock users can quickly implement using just a few key building blocks and existing logs using FMs, Amazon Bedrock Knowledge Bases , Amazon Bedrock Guardrails , and Amazon Bedrock Agents.

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AWS Machine Learning - AI

DECEMBER 23, 2024

Lettria , an AWS Partner, demonstrated that integrating graph-based structures into RAG workflows improves answer precision by up to 35% compared to vector-only retrieval methods. Using AWS and Lettria for enhanced RAG applications In this section, we discuss how you can use AWS and Lettria for enhanced RAG applications.

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AWS Machine Learning - AI

APRIL 23, 2024

The AWS Well-Architected Framework provides best practices and guidelines for designing and operating reliable, secure, efficient, and cost-effective systems in the cloud. This post explores the new enterprise-grade features for Knowledge Bases on Amazon Bedrock and how they align with the AWS Well-Architected Framework.

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AWS Machine Learning - AI

FEBRUARY 8, 2024

You can now use Agents for Amazon Bedrock and Knowledge Bases for Amazon Bedrock to configure specialized agents that seamlessly run actions based on natural language input and your organization’s data. Knowledge Bases for Amazon Bedrock provides fully managed RAG to supply the agent with access to your data.

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AWS Machine Learning - AI

MARCH 1, 2024

At AWS re:Invent 2023, we announced the general availability of Knowledge Bases for Amazon Bedrock. With a knowledge base, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for fully managed Retrieval Augmented Generation (RAG). billion, $6.1 billion, and $5.9

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AWS Machine Learning - AI

MAY 31, 2024

With the Amazon Bedrock serverless experience, you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using the Amazon Web Services (AWS) tools without having to manage infrastructure. The following diagram depicts a high-level RAG architecture.

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AWS Machine Learning - AI

NOVEMBER 20, 2024

One of its key features, Amazon Bedrock Knowledge Bases , allows you to securely connect FMs to your proprietary data using a fully managed RAG capability and supports powerful metadata filtering capabilities. Context recall – Assesses the proportion of relevant information retrieved from the knowledge base.

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AWS Machine Learning - AI

FEBRUARY 29, 2024

Knowledge Bases for Amazon Bedrock allows you to build performant and customized Retrieval Augmented Generation (RAG) applications on top of AWS and third-party vector stores using both AWS and third-party models. RAG is a popular technique that combines the use of private data with large language models (LLMs).

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AWS Machine Learning - AI

SEPTEMBER 25, 2024

Amazon Bedrock Knowledge Bases is a fully managed capability that helps you implement the entire RAG workflow—from ingestion to retrieval and prompt augmentation—without having to build custom integrations to data sources and manage data flows. Latest innovations in Amazon Bedrock Knowledge Base provide a resolution to this issue.

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AWS Machine Learning - AI

JULY 2, 2024

In November 2023, we announced Knowledge Bases for Amazon Bedrock as generally available. Knowledge bases allow Amazon Bedrock users to unlock the full potential of Retrieval Augmented Generation (RAG) by seamlessly integrating their company data into the language model’s generation process.

Knowledge Base 123

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AWS Machine Learning - AI

APRIL 26, 2024

At AWS re:Invent 2023, we announced the general availability of Knowledge Bases for Amazon Bedrock. With Knowledge Bases for Amazon Bedrock, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for fully managed Retrieval Augmented Generation (RAG).

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AWS Machine Learning - AI

JULY 1, 2024

Generative artificial intelligence (AI)-powered chatbots play a crucial role in delivering human-like interactions by providing responses from a knowledge base without the involvement of live agents. You can simply connect QnAIntent to company knowledge sources and the bot can immediately handle questions using the allowed content.

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AWS Machine Learning - AI

FEBRUARY 19, 2024

One way to enable more contextual conversations is by linking the chatbot to internal knowledge bases and information systems. Integrating proprietary enterprise data from internal knowledge bases enables chatbots to contextualize their responses to each user’s individual needs and interests.

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AWS Machine Learning - AI

NOVEMBER 1, 2024

Although tagging is supported on a variety of Amazon Bedrock resources —including provisioned models, custom models, agents and agent aliases, model evaluations, prompts, prompt flows, knowledge bases, batch inference jobs, custom model jobs, and model duplication jobs—there was previously no capability for tagging on-demand foundation models.

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AWS Machine Learning - AI

MARCH 20, 2025

Cross-Region inference enables seamless management of unplanned traffic bursts by using compute across different AWS Regions. Amazon Bedrock Data Automation optimizes for available AWS Regional capacity by automatically routing across regions within the same geographic area to maximize throughput at no additional cost.

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AWS Machine Learning - AI

NOVEMBER 15, 2024

At AWS, we are committed to developing AI responsibly , taking a people-centric approach that prioritizes education, science, and our customers, integrating responsible AI across the end-to-end AI lifecycle. For human-in-the-loop evaluation, which can be done by either AWS managed or customer managed teams, you must bring your own dataset.

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AWS Machine Learning - AI

AUGUST 5, 2024

Amazon Transcribe is an AWS AI service that makes it straightforward to convert speech to text. In this post, we show how Amazon Transcribe and Amazon Bedrock can streamline the process to catalog, query, and search through audio programs, using an example from the AWS re:Think podcast series. and the AWS SDK for Python (Boto3).

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AWS Machine Learning - AI

JULY 10, 2024

Knowledge Bases for Amazon Bedrock is a fully managed service that helps you implement the entire Retrieval Augmented Generation (RAG) workflow from ingestion to retrieval and prompt augmentation without having to build custom integrations to data sources and manage data flows, pushing the boundaries for what you can do in your RAG workflows.

Knowledge Base 129

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AWS Machine Learning - AI

AUGUST 28, 2024

The complexity of developing and deploying an end-to-end RAG solution involves several components, including a knowledge base, retrieval system, and generative language model. Solution overview The solution provides an automated end-to-end deployment of a RAG workflow using Knowledge Bases for Amazon Bedrock.

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AWS Machine Learning - AI

JANUARY 13, 2025

Amazon Q Business as a web experience makes AWS best practices readily accessible, providing cloud-centered recommendations quickly and making it straightforward to access AWS service functions, limits, and implementations. Amazon Q then offered personalized suggestions based on this FinOps persona perspective.

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AWS Machine Learning - AI

NOVEMBER 20, 2024

In this post, we explore how you can use Amazon Q Business , the AWS generative AI-powered assistant, to build a centralized knowledge base for your organization, unifying structured and unstructured datasets from different sources to accelerate decision-making and drive productivity.

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AWS Machine Learning - AI

MARCH 20, 2025

It integrates with existing applications and includes key Amazon Bedrock features like foundation models (FMs), prompts, knowledge bases, agents, flows, evaluation, and guardrails. The workflow is as follows: The user logs into SageMaker Unified Studio using their organizations SSO from AWS IAM Identity Center.

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AWS Machine Learning - AI

NOVEMBER 14, 2024

Solution overview This solution uses the Amazon Bedrock Knowledge Bases chat with document feature to analyze and extract key details from your invoices, without needing a knowledge base. Prerequisites To perform this solution, complete the following: Create and activate an AWS account. Install Python 3.7

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AWS Machine Learning - AI

NOVEMBER 6, 2024

The challenge: Enabling self-service cloud governance at scale Hearst undertook a comprehensive governance transformation for their Amazon Web Services (AWS) infrastructure. The CCoE implemented AWS Organizations across a substantial number of business units. About the Authors Steven Craig is a Sr. Director, Cloud Center of Excellence.

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CIO

NOVEMBER 1, 2024

As part of MMTech’s unifying strategy, Beswick chose to retire the data centers and form an “enterprisewide architecture organization” with a set of standards and base layers to develop applications and workloads that would run on the cloud, with AWS as the firm’s primary cloud provider.

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AWS Machine Learning - AI

MARCH 11, 2025

OpenAI launched GPT-4o in May 2024, and Amazon introduced Amazon Nova models at AWS re:Invent in December 2024. One of the most critical applications for LLMs today is Retrieval Augmented Generation (RAG), which enables AI models to ground responses in enterprise knowledge bases such as PDFs, internal documents, and structured data.

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AWS Machine Learning - AI

APRIL 18, 2024

Tools like Terraform and AWS CloudFormation are pivotal for such transitions, offering infrastructure as code (IaC) capabilities that define and manage complex cloud environments with precision. AWS Landing Zone addresses this need by offering a standardized approach to deploying AWS resources.

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AWS Machine Learning - AI

SEPTEMBER 19, 2024

Amazon Bedrock Knowledge Bases provides foundation models (FMs) and agents in Amazon Bedrock contextual information from your company’s private data sources for Retrieval Augmented Generation (RAG) to deliver more relevant, accurate, and customized responses. Amazon Bedrock Knowledge Bases offers a fully managed RAG experience.

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CIO

DECEMBER 9, 2024

In this scenario, using AI to improve employee capabilities by building on the existing knowledge base will be key. In 2025, we can expect to see better frameworks for calculating these costs from firms such as Gartner, IDC, and Forrester that build on their growing knowledge bases from proofs of concept and early deployments.

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AWS Machine Learning - AI

OCTOBER 11, 2024

It uses Amazon Bedrock , AWS Health , AWS Step Functions , and other AWS services. Some examples of AWS-sourced operational events include: AWS Health events — Notifications related to AWS service availability, operational issues, or scheduled maintenance that might affect your AWS resources.

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CIO

NOVEMBER 4, 2024

Camelot has the flexibility to run on any selected GenAI LLM across cloud providers like AWS, Microsoft Azure, and GCP (Google Cloud Platform), ensuring that the company meets compliance regulations for data security. However, integrating Myrddin into the CMMC dashboard was just the beginning.

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AWS Machine Learning - AI

SEPTEMBER 24, 2024

Working with the AWS Generative AI Innovation Center , DoorDash built a solution to provide Dashers with a low-latency self-service voice experience to answer frequently asked questions, reducing the need for live agent assistance, in just 2 months. You can deploy the solution in your own AWS account and try the example solution.

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