This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB. We walk you through our solution, detailing the core logic of the Lambda functions. Amazon S3 invokes the {stack_name}-create-batch-queue-{AWS-Region} Lambda function.
By implementing this architectural pattern, organizations that use Google Workspace can empower their workforce to access groundbreaking AI solutions powered by Amazon Web Services (AWS) and make informed decisions without leaving their collaboration tool. This request contains the user’s message and relevant metadata.
Such a virtual assistant should support users across various business functions, such as finance, legal, human resources, and operations. This architecture workflow includes the following steps: A user submits a question through a web or mobile application. The architecture of this system is illustrated in the following figure.
To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloud architectures. The solution incorporates the following key features: Using a Retrieval Augmented Generation (RAG) architecture, the system generates a context-aware detailed assessment.
Solution overview The following architecture diagram represents the high-level design of a solution proven effective in production environments for AWS Support Engineering. The following diagram illustrates an example architecture for ingesting data through an endpoint interfacing with a large corpus.
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.
It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. Depending on the use case and data isolation requirements, tenants can have a pooled knowledge base or a siloed one and implement item-level isolation or resource level isolation for the data respectively.
Solution overview This section outlines the architecture designed for an email support system using generative AI. The AI engine accesses this resource to pull relevant information needed to effectively address customer inquiries. Deploy the AWS CDK project to provision the required resources in your AWS account.
The solution also uses Amazon Cognito user pools and identity pools for managing authentication and authorization of users, Amazon API Gateway REST APIs, AWS Lambda functions, and an Amazon Simple Storage Service (Amazon S3) bucket. The following diagram illustrates the architecture of the application.
Time-consuming and resource-intensive The process required dedicating significant time and resources to review the submissions manually and follow up with institutions to request additional information if needed to rectify the submissions, resulting in slowing down the overall review process.
Architecture Overview The accompanying diagram visually represents our infrastructure’s architecture, highlighting the relationships between key components. Until then, we encourage you to experiment with the techniques covered and explore the resources below for deeper understanding.
This post will discuss agentic AI driven architecture and ways of implementing. Agentic AI architecture Agentic AI architecture is a shift in process automation through autonomous agents towards the capabilities of AI, with the purpose of imitating cognitive abilities and enhancing the actions of traditional autonomous agents.
Additionally, we use various AWS services, including AWS Amplify for hosting the front end, AWS Lambda functions for handling request logic, Amazon Cognito for user authentication, and AWS Identity and Access Management (IAM) for controlling access to the agent. The function uses a geocoding service or database to perform this lookup.
Lets look at an example solution for implementing a customer management agent: An agentic chat can be built with Amazon Bedrock chat applications, and integrated with functions that can be quickly built with other AWS services such as AWS Lambda and Amazon API Gateway. Then the user interacts with the chat application using natural language.
Solution deployment This solution includes an AWS CloudFormation template that streamlines the deployment of required AWS resources, providing consistent and repeatable deployments across environments. Multiple programming language support – The GitHub repository provides the observability solution in both Python and Node.js
Accelerate building on AWS What if your AI assistant could instantly access deep AWS knowledge, understanding every AWS service, best practice, and architectural pattern? Lets create an architecture that uses Amazon Bedrock Agents with a custom action group to call your internal API. Transform how you build on AWS today.
Fargate vs. Lambda has recently been a trending topic in the serverless space. Fargate and Lambda are two popular serverless computing options available within the AWS ecosystem. While both tools offer serverless computing, they differ regarding use cases, operational boundaries, runtime resource allocations, price, and performance.
The following diagram illustrates the solution architecture. Pre-annotation and post-annotation AWS Lambda functions are optional components that can enhance the workflow. The pre-annotation Lambda function can process the input manifest file before data is presented to annotators, enabling any necessary formatting or modifications.
I summarized my key takeaways that can help you improve your serverless architectures. From Lambda-lith to Step Function A common anti-pattern in serverless architecture is creating a “Lambda-lith” – a monolithic Lambda function that handles too many responsibilities.
Designed with a serverless, cost-optimized architecture, the platform provisions SageMaker endpoints dynamically, providing efficient resource utilization while maintaining scalability. The following diagram illustrates the solution architecture. Click here to open the AWS console and follow along.
With a vast array of services and resource footprints spanning hundreds of accounts, organizations can face an overwhelming volume of operational events occurring daily, making manual administration impractical. AWS Trusted Advisor findings — Opportunities for optimizing your AWS resources, improving security, and reducing costs.
Troubleshooting infrastructure as code (IaC) errors often consumes valuable time and resources. Solution overview Before we dive into the deployment process, lets walk through the key steps of the architecture as illustrated in the following figure. This contextual information is then sent back to the first Lambda function.
The solution offers the following potential benefits: Efficiency By automating the initial protocol design process, researchers can save valuable time and resources, allowing them to focus on more critical aspects of clinical trial execution. Solution overview The following diagram illustrates the solution architecture.
This solution shows how Amazon Bedrock agents can be configured to accept cloud architecture diagrams, automatically analyze them, and generate Terraform or AWS CloudFormation templates. Solution overview Before we explore the deployment process, let’s walk through the key steps of the architecture as illustrated in Figure 1.
The following diagram illustrates the conceptual architecture of an AI assistant with Amazon Bedrock IDE. Solution architecture The architecture in the preceding figure shows how Amazon Bedrock IDE orchestrates the data flow. With these resources ready, you can create your sales analytics application.
In this post, we describe how CBRE partnered with AWS Prototyping to develop a custom query environment allowing natural language query (NLQ) prompts by using Amazon Bedrock, AWS Lambda , Amazon Relational Database Service (Amazon RDS), and Amazon OpenSearch Service. A Lambda function with business logic invokes the primary Lambda function.
Migrating to the cloud is an essential step for modern organizations aiming to capitalize on the flexibility and scale of cloud resources. With Amazon Bedrock, teams can input high-level architectural descriptions and use generative AI to generate a baseline configuration of Terraform scripts.
API gateways can provide loose coupling between model consumers and the model endpoint service, and flexibility to adapt to changing model, architectures, and invocation methods. In this post, we show you how to build an internal SaaS layer to access foundation models with Amazon Bedrock in a multi-tenant (team) architecture.
Serverless architecture is a way of building and running applications without the need to manage infrastructure. AWS offers various serverless services, with AWS Lambda being one of the most prominent. There's no idle capacity because billing is based on the actual amount of resources consumed by an application.
Integrating it with the range of AWS serverless computing, networking, and content delivery services like AWS Lambda , Amazon API Gateway , and AWS Amplify facilitates the creation of an interactive tool to generate dynamic, responsive, and adaptive logos. Solution overview The following diagram illustrates the solution architecture.
In this post, we show you how to build a speech-capable order processing agent using Amazon Lex, Amazon Bedrock, and AWS Lambda. Solution overview The following diagram illustrates our solution architecture. This can be done with a Lambda layer or by using a specific AMI with the required libraries. awscli>=1.29.57
The good news is that deploying these applications on a serverless architecture can make it easier to protect them. Cloud-native architecture has opened up new avenues for developers, bringing individual components out of monolithic server configurations and making them readily available as consumable services. Here’s why.
Workflow Overview Write Infrastructure Code (Python) Pulumi Translates Code to AWS Resources Apply Changes (pulumi up) Pulumi Tracks State for Future Updates Prerequisites Pulumi Dashboard The Pulumi Dashboard (if using Pulumi Cloud) helps track: The current state of infrastructure. Components in the architecture.
Seamless live stream acquisition The solution begins with an IP-enabled camera capturing the live event feed, as shown in the following section of the architecture diagram. A serverless, event-driven workflow using Amazon EventBridge and AWS Lambda automates the post-event processing.
We will deep dive into the MCP architecture later in this post. Using a client-server architecture (as illustrated in the following screenshot), MCP helps developers expose their data through lightweight MCP servers while building AI applications as MCP clients that connect to these servers.
The following diagram provides a simplified view of the solution architecture and highlights the key elements. The DynamoDB update triggers an AWS Lambda function, which starts a Step Functions workflow. The Step Functions workflow invokes a Lambda function to generate a status report. height – The height of the image in pixels.
The following diagram illustrates the solution architecture. Amazon SQS enables a fault-tolerant decoupled architecture. The WebSocket triggers an AWS Lambda function, which creates a record in Amazon DynamoDB. Another Lambda function gets triggered with a new message in the SQS queue.
The modern architecture of databases makes this complicated, with information potentially distributed across Kubernetes containers, Lambda, ECS and EC2 and more. The company works with big companies that need to handle large amounts of data, typically across hybrid environments across containers and clouds, in real time.
The popular architecture pattern of Retrieval Augmented Generation (RAG) is often used to augment user query context and responses. Internally, Amazon Bedrock uses embeddings stored in a vector database to augment user query context at runtime and enable a managed RAG architecture solution.
The steps could be AWS Lambda functions that generate prompts, parse foundation models’ output, or send email reminders using Amazon SES. Overview of solution Figure 1: Solution architecture As shown in Figure 1, the workflow starts from the Amazon API Gateway , then goes through different steps in the Step Functions state machine.
One such service is their serverless computing service , AWS Lambda. For the uninitiated, Lambda is an event-driven serverless computing platform that lets you run code without managing or provisioning servers and involves zero administration. How does AWS Lambda Work. Why use AWS Lambda? Read on to know. zip or jar.
Our most-used AWS resources will help you stay on track in your journey to learn and apply AWS. We dove into the data on our online learning platform to identify the most-used Amazon Web Services (AWS) resources. Continue reading 10 top AWS resources on O’Reilly’s online learning platform.
The magic happens through a combination of Serverless, user input, a CloudFront distribution, a Lambda function, and the OpenAI API. The Lambda function is a Python script that incorporates the Xebia mission, vision, and values, as well as each leader’s personality and speaking style. provider: name: aws runtime: python3.9
This significantly reduces the burden on human resources and allows employees to focus on more strategic and creative tasks. The code and resources required for deployment are available in the amazon-bedrock-examples repository. The following diagram illustrates the solution architecture.
We organize all of the trending information in your field so you don't have to. Join 49,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content