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This week on our Friends on Friday guest blog post my colleague, Dan Rood, writes about the precious commodity of time and why we must use the latest technologies to help customers save time and have amazing customer experiences. Standardized web services and APIs for federating silos of data and connecting applications ease integration.
Consider the number of critical APIs that are embedded. Double the APIs – quadruple your problems! The post Guest Blog: Winning Tactics for CX Vendor Selection appeared first on Shep Hyken. Is it robust enough for your scale and complexity of operations? Does the vendor have a proven track record with your legacy systems?
It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic. API Gateway also provides a WebSocket API. Incoming requests to the gateway go through this point.
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. Note that these APIs use objects as namespaces, alleviating the need for explicit imports.
In this blog post, we’ll focus on Amazon Bedrock IDE and its generative AI capabilities within the Amazon SageMaker Unified Studio environment. This includes setting up Amazon API Gateway , AWS Lambda functions, and Amazon Athena to enable querying the structured sales data. Download all three sample data files.
This involves creating an OAuth API endpoint in ServiceNow and using the web experience URL from Amazon Q Business as the callback URL. The final step of the solution involves enhancing the application environment with a custom plugin for ServiceNow using APIs defined in an OpenAPI schema.
A Dashboard Designed for Developers APIs serve as the bridges that enable different software systems to communicate, facilitating the flow of data and functionality. For developers, APIs are the… Read more on Cisco Blogs
Meta just announced that the WhatsApp Cloud API and businesses across the world [.]. The post WhatsApp Cloud API : Everything you need to know about it appeared first on Kommunicate Blog.
Last Updated on June 22, 2023 What is a Chatbot API? A chatbot API is a set of protocols that allow developers to access the functionalities of a chatbot. A chatbot API enables seamless integration into various applications, systems or platforms by standardizing the way you send, receive and extract messages via the chatbot.
In this post, we describe the enhancements to the forecasting capabilities of SageMaker Canvas and guide you on using its user interface (UI) and AutoML APIs for time-series forecasting. While the SageMaker Canvas UI offers a code-free visual interface, the APIs empower developers to interact with these features programmatically.
In this post, we will continue to build on top of the previous solution to demonstrate how to build a private API Gateway via Amazon API Gateway as a proxy interface to generate and access Amazon SageMaker presigned URLs. The user invokes createStudioPresignedUrl API on API Gateway along with a token in the header.
In previous blog post , we described an end-to-end identity verification solution in a single AWS Region. The solution uses the Amazon Rekognition APIs DetectFaces for face detection and CompareFaces for face comparison. We think of those APIs as stateless APIs because they don’t depend on an Amazon Rekognition face collection.
To learn more about opportunities for customers to use SLMs, see Opportunities for telecoms with small language models: Insights from AWS and Meta on our AWS Industries blog. The embedding model, which is hosted on the same EC2 instance as the local LLM API inference server, converts the text chunks into vector representations.
Agent Creator is a versatile extension to the SnapLogic platform that is compatible with modern databases, APIs, and even legacy mainframe systems, fostering seamless integration across various data environments. The integration with Amazon Bedrock is achieved through the Amazon Bedrock InvokeModel APIs.
This blog post delves into how these innovative tools synergize to elevate the performance of your AI applications, ensuring they not only meet but exceed the exacting standards of enterprise-level deployments. This blog post focuses on using its Observability / Evaluation modules.
This blog post is co-written with Renuka Kumar and Thomas Matthew from Cisco. The end-user sends their natural language queries to the NL2SQL solution using a REST API. Amazon API Gateway is used to provision the REST API, which can be secured by Amazon Cognito. The following diagram illustrates the solution architecture.
This enables sales teams to interact with our internal sales enablement collateral, including sales plays and first-call decks, as well as customer references, customer- and field-facing incentive programs, and content on the AWS website, including blog posts and service documentation.
10 Chatbot API. The last of the chatbot features we’ll cover is chatbot API. A simple chatbot builder is a great first step, but chatbot API (application programming interface) can take things up a notch. Chatbot API is a framework that allows you to create a conversational experience through more advanced programming languages.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
Amazon Bedrock , a fully managed service offering high-performing foundation models from leading AI companies through a single API, has recently introduced two significant evaluation capabilities: LLM-as-a-judge under Amazon Bedrock Model Evaluation and RAG evaluation for Amazon Bedrock Knowledge Bases.
Fine-tune an Amazon Nova model using the Amazon Bedrock API In this section, we provide detailed walkthroughs on fine-tuning and hosting customized Amazon Nova models using Amazon Bedrock. We first provided a detailed walkthrough on how to fine-tune, host, and conduct inference with customized Amazon Nova through the Amazon Bedrock API.
The Amazon Bedrock single API access, regardless of the models you choose, gives you the flexibility to use different FMs and upgrade to the latest model versions with minimal code changes. Amazon Titan FMs provide customers with a breadth of high-performing image, multimodal, and text model choices, through a fully managed API.
Luckily for us, Vonage has a fantastic API for tracking phone calls ! We’ll use the Vonage API and build a.NET Core application that stores and displays this information by using event sourcing. Vonage API Account. To complete this tutorial, you will need a Vonage API account. Prerequisites. The latest version of.NET /.NET
The Amazon Bedrock API returns the output Q&A JSON file to the Lambda function. The container image sends the REST API request to Amazon API Gateway (using the GET method). API Gateway communicates with the TakeExamFn Lambda function as a proxy. The JSON file is returned to API Gateway.
For your reference, this blog post demonstrates a solution to create a VPC with no internet connection using an AWS CloudFormation template. Note that MLflow tracking starts from the mlflow.start_run() API. The mlflow.autolog() API can automatically log information such as metrics, parameters, and artifacts.
Refer to Getting started with the API to set up your environment to make Amazon Bedrock requests through the AWS API. Test the code using the native inference API for Anthropics Claude The following code uses the native inference API to send a text message to Anthropics Claude. client = boto3.client("bedrock-runtime",
Additionally, Q Business conversation APIs employ a layer of privacy protection by leveraging trusted identity propagation enabled by IAM Identity Center. Amazon Q Business comes with rich API support to perform administrative tasks or to build an AI-assistant with customized user experience for your enterprise.
In this blog post, you will learn about prompt chaining, how to break a complex task into multiple tasks to use prompt chaining with an LLM in a specific order, and how to involve a human to review the response generated by the LLM. Detect if the review content has any harmful information using the Amazon Comprehend DetectToxicContent API.
This solution ingests and processes data from hundreds of thousands of support tickets, escalation notices, public AWS documentation, re:Post articles, and AWS blog posts. Step Functions orchestrates AWS services like AWS Lambda and organization APIs like DataStore to ingest, process, and store data securely.
Based on OpenSearch blog post , hybrid search improves result quality by 812% compared to keyword search and by 15% compared to natural language search. Send the text, images, and metadata to Amazon Bedrock using its API to generate embeddings using the Amazon Titan Multimodal Embeddings G1 model.
Moreover, this capability prioritizes the connected Amazon Bedrock API source/primary region when possible, helping to minimize latency and improve responsiveness. Compatibility with existing Amazon Bedrock API No additional routing or data transfer cost and you pay the same price per token for models as in your source/primary region.
Blogs – Each blog is considered a single document. Similarly for pages and blogs, you use the restrictions page. For more information about page and blog restrictions, see Page Restrictions on the Confluence Support website. _user_id – Usernames are present on the space, page, or blog where there are restrictions.
This is a guest blog post co-written with Jordan Knight, Sara Reynolds, George Lee from Travelers. Foundation models (FMs) are used in many ways and perform well on tasks including text generation, text summarization, and question answering.
Large language model (LLM) agents are programs that extend the capabilities of standalone LLMs with 1) access to external tools (APIs, functions, webhooks, plugins, and so on), and 2) the ability to plan and execute tasks in a self-directed fashion. Note that the next action may or may not involve using a tool or API.
This blog post is co-written with Gene Arnold from Alation. With the connector ready, move over to the SageMaker Studio notebook and perform data synchronization operations by invoking Amazon Q Business APIs. First, you would need build connectors to the data sources. secrets_manager_client = boto3.client('secretsmanager')
This blog post is co-written with Louis Prensky and Philip Kang from Appian. This blog post will cover how Appian AI skills build automation into organizations mission-critical processes to improve operational excellence, reduce costs, and build scalable solutions.
This blog post shares more about how generative AI solutions from Amazon Ads help brands create more visually rich consumer experiences. In this blog post, we describe the architectural and operational details of how Amazon Ads implemented its generative AI-powered image creation solution on AWS.
Users can also interact with data with ODBC, JDBC, or the Amazon Redshift Data API. In this blog post, we will show you how to use both of these services together to efficiently perform analysis on massive data sets in the cloud while addressing the challenges mentioned above. Solution overview. Just load your data and start querying.
This blog post outlines various use cases where we’re using generative AI to address digital publishing challenges. Our CMS backend Nova is implemented using Amazon API Gateway and several AWS Lambda functions. Amazon DynamoDB serves as the primary database for 20 Minutes articles.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a unified API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
For instance, Rails can handle complex business logic and APIs, while React takes care of delivering a smooth, interactive user experience. Separation of Concerns: Rails efficiently handles the backend logic, data processing, and APIs, while React manages the front-end user interface.
While for readers of blogs or online magazines, such quality does not seem so special, when it comes to customers whose businesses largely depend on the reliability of third-party services like payment systems, booking APIs, server and hosting providers, this comes of enormous importance. .
In this blog post, we demonstrate how to build a unified translation solution with customization features using Amazon Translate and other AWS services. Using Amazon API Gateway , these features are exposed as one simple /translate API. Therefore, another API /customterm is exposed. API Gateway validates the API key.
This blog post discusses how BMC Software added AWS Generative AI capabilities to its product BMC AMI zAdviser Enterprise. Aggregate the data retrieved from Elasticsearch and form the prompt for the generative AI Amazon Bedrock API call. Check out the BMC website to learn more and set up a demo.
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