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
In this post, we guide you through integrating Amazon Bedrock Agents with enterprise data APIs to create more personalized and effective customer support experiences. An automotive retailer might use inventory management APIs to track stock levels and catalog APIs for vehicle compatibility and specifications.
The solution uses the FMs tool use capabilities, accessed through the Amazon Bedrock Converse API. This enables the FMs to not just process text, but to actively engage with various external tools and APIs to perform complex document analysis tasks. For more details on how tool use works, refer to The complete tool use workflow.
adds new APIs to customize GraphStorm pipelines: you now only need 12 lines of code to implement a custom node classification training loop. Based on customer feedback for the experimental APIs we released in GraphStorm 0.2, introduces refactored graph ML pipeline APIs. Specifically, GraphStorm 0.3 In addition, GraphStorm 0.3
Enabling Global Resiliency for an Amazon Lex bot is straightforward using the AWS Management Console , AWS Command Line Interface (AWS CLI), or APIs. Global Resiliency APIs Global Resiliency provides API support to create and manage replicas. To better understand the solution, refer to the following architecture diagram.
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, 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, 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 the post Secure Amazon SageMaker Studio presigned URLs Part 2: Private API with JWT authentication , we demonstrated how to build a private API to generate Amazon SageMaker Studio presigned URLs that are only accessible by an authenticated end-user within the corporate network from a single account.
Amazon Bedrock is a fully managed service that makes a wide range of foundation models (FMs) available though an API without having to manage any infrastructure. Amazon API Gateway and AWS Lambda to create an API with an authentication layer and integrate with Amazon Bedrock. An API created with Amazon API Gateway.
Solution overview Our solution implements a verified semantic cache using the Amazon Bedrock Knowledge Bases Retrieve API to reduce hallucinations in LLM responses while simultaneously improving latency and reducing costs. The function checks the semantic cache (Amazon Bedrock Knowledge Bases) using the Retrieve API.
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, 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.
Challenge 2: Integration with Wearables and Third-Party APIs Many people use smartwatches and heart rate monitors to measure sleep, stress, and physical activity, which may affect mental health. Third-party APIs may link apps to healthcare and meditation services. However, integrating these diverse sources is not straightforward.
Programmatic setup Alternatively, you can create your labeling job programmatically using the CreateLabelingJob API. Whether you choose the SageMaker console or API approach, the result is the same: a fully configured labeling job ready for your annotation team. documentation. When implementing additional Wavesurfer.js
We suggest consulting LLM prompt engineering documentation such as Anthropic prompt engineering for experiments. Refer to Getting started with the API to set up your environment to make Amazon Bedrock requests through the AWS API. I've immediately revoked the compromised API credentials and initiated our security protocol.
The Amazon Lex fulfillment AWS Lambda function retrieves the Talkdesk touchpoint ID and Talkdesk OAuth secrets from AWS Secrets Manager and initiates a request to Talkdesk Digital Connect using the Start a Conversation API. If the request to the Talkdesk API is successful, a Talkdesk conversation ID is returned to Amazon Lex.
Second, integration tests verify the end-to-end flow of the REST API and the chatbots interaction with the large language model (LLM). This allowed them to quickly move their API-based backend services to a cloud-native environment. Anwar Rizal is a Senior Machine Learning consultant for AWS Professional Services based in Paris.
However, there are benefits to building an FM-based classifier using an API service such as Amazon Bedrock, such as the speed to develop the system, the ability to switch between models, rapid experimentation for prompt engineering iterations, and the extensibility into other related classification tasks.
A: Leading call centers use API integrations and secure portals to sync with electronic health record (EHR) systems, enabling real-time updates and streamlined data sharing. Contact us today to schedule a free consultation. Q3: How do call centers integrate with EHR systems?
We recommend running similar scripts only on your own data sources after consulting with the team who manages them, or be sure to follow the terms of service for the sources that youre trying to fetch data from. Solution overview The solution shown of integrating Alations business policies is for demonstration purposes only.
WEM is becoming an open enterprise-grade platform of interoperable applications built with microservices and application programming interfaces (APIs). Driving the strategic direction of the customer experience (CX) is at the core of DMG’s extensive consultation and collaboration with executives, leaders, and industry innovators.
From our experience, it is the framing phase that is the most time-consuming as you have to consult with all the teams involved in the project and obtain various approvals to start the developments. How long does it take to deploy an AI chatbot? Poor technical documentation.
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 single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
The solution uses the following services: Amazon API Gateway is a fully managed service that makes it easy for developers to publish, maintain, monitor, and secure APIs at any scale. Purina’s solution is deployed as an API Gateway HTTP endpoint, which routes the requests to obtain pet attributes.
At the forefront of this evolution sits Amazon Bedrock , a fully managed service that makes high-performing foundation models (FMs) from Amazon and other leading AI companies available through an API. System integration – Agents make API calls to integrated company systems to run specific actions.
To use a specific LLM from Amazon Bedrock, SageMaker Canvas uses the model ID of the chosen LLM as part of the API calls. Limit access to all Amazon Bedrock models To restrict access to all Amazon Bedrock models, you can modify the SageMaker role to explicitly deny these APIs. This prevents the creation of endpoints using these models.
OPEN - Does your recorder support the collection of non-audio data (such as CRM, ACD or agent desktop applications) via REST API, which can be appended to audio recordings? To learn more about the capabilities or inabilities of your current audio capture environment, click below for a free consultation.
In the architecture shown in the following diagram, users input text in the React -based web app, which triggers Amazon API Gateway , which in turn invokes an AWS Lambda function depending on the bias in the user text. Additionally, it highlights the specific parts of your input text related to each category of bias.
This layer encapsulates the logic required to interact with the AWS AI services to manage API calls, data formatting, and error handling. Philip Kang is a Principal Solutions Consultant in Partner Technology & Innovation centers with Appian. Louis Prensky is a Senior Product Manager at Appian.
The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.
It’s a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like Anthropic, Cohere, Meta, Mistral 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.
We partnered with Keepler , a cloud-centered data services consulting company specialized in the design, construction, deployment, and operation of advanced public cloud analytics custom-made solutions for large organizations, in the creation of the first generative AI solution for one of our corporate teams.
The endpoints like SageMaker API, SageMaker Studio, and SageMaker notebook facilitate secure and reliable communication between the platform account’s VPC and the SageMaker domain managed by AWS in the SageMaker service account. This makes a REST API call to API Gateway and generates a presigned URL to access the SageMaker domain.
Create an end-to-end seamless customer experience using integrations and open API platforms like TechSee Visual Journeys. APIs and integrations allow you to provide consistent visual guidance across every channel, maintain your branding and use the visual data to improve the next stage of the customer’s interaction.
By the end of the consulting engagement, the team had implemented the following architecture that effectively addressed the core requirements of the customer team, including: Code Sharing – SageMaker notebooks enable data scientists to experiment and share code with other team members.
Amazon Bedrock is a fully managed service that makes foundational models (FMs) from leading artificial intelligence (AI) companies and Amazon available through an API, so you can choose from a wide range of FMs to find the model that’s best suited for your use case. If you need assistance or guidance, reach out to an AWS representative.
VI’s automated insights are natively integrated across the TechSee platform and can be fully integrated into any business application via API. They can simply upload a photo through a Visual Journey or even your chatbot (using APIs), and VI can visually understand what is wrong. This is AI built for practical impact.
DMG Consulting Releases 2018 – 2019 Cloud-Based Contact Center Infrastructure Product and Market Report. Who: DMG Consulting LLC, a leading provider of contact center, back-office and real-time analytics market research and consulting services. Where: Available at the DMG Consulting online store.
DMG Consulting Releases 2021 – 2022 Cloud-Based Contact Center Infrastructure Product and Market Report. Who: DMG Consulting LLC, a leading provider of contact center and back-office market research and consulting services. Where: Available at the DMG Consulting online store. About DMG Consulting LLC. MEDIA ALERT.
You can now use cross-account support for Amazon SageMaker Pipelines to share pipeline entities across AWS accounts and access shared pipelines directly through Amazon SageMaker API calls. The data scientist is now able to describe and monitor the test pipeline run status using SageMaker API calls from the dev account.
Xentaurs is a next generation consulting and Cisco digital solution integrator partner dedicated to making digital technology transformation a reality. Data breaches, ransomware and other modern cybersecurity threats have dramatically changed the challenges financial institutions and IT teams must solve.
We present our solution through a fictional consulting company, OneCompany Consulting, using automatically generated personalized website content for accelerating business client onboarding for their consultancy service. For this post, we use Anthropic’s Claude models on Amazon Bedrock. Our core values are: 1.
Our latest product innovation, Transaction Risk API , was specifically built for easy integration into sophisticated machine learning (ML) models and is designed to help eCommerce merchants, marketplaces, payment processors, and others manage payment fraud. Transaction Risk API delivers a response within 100 ms to meet this need.
LMA for healthcare is an extended version of the Live Meeting Assistant solution that has been adapted to generate clinical notes automatically during virtual doctor-patient consultations. In the future, we expect LMA for healthcare to use the AWS HealthScribe API in addition to other AWS services.
Local cultural consultants help align content. Technological Complexities Translation integration and access might be hampered by disparate systems, changing APIs, coding errors, content control restrictions, inconsistent workflows, and reporting. Continuous IT cooperation is vital.
A Forrester Consulting study found that 70% of contact center agents lack access to relevant customer data. Once your core flows are set up, the Editor’s integrated low-code capabilities allow your developers to expand further with Visual AI, automated guidance, API integrations, and more. What is an automated visual flow?
We organize all of the trending information in your field so you don't have to. Join 34,000+ 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