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Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs

AWS Machine Learning

In this post, we guide you through integrating Amazon Bedrock Agents with enterprise data APIs to create more personalized and effective customer support experiences. Although the principles discussed are applicable across various industries, we use an automotive parts retailer as our primary example throughout this post.

APIs 127
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Automate chatbot for document and data retrieval using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

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.

Chatbots 129
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Amazon Rekognition Labels adds 600 new labels, including landmarks, and now detects dominant colors

AWS Machine Learning

The following table shows the labels and confidence scores returned in the API response. Vehicles and automotive – Truck, Wheel, Tire, Bumper, Car Seat, Car Mirror, etc. By default, the API returns up to 10 dominant colors unless you specify the number of colors to return. Confidence Scores. Brooklyn Bridge. Improved labels.

APIs 96
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Get insights on your user’s search behavior from Amazon Kendra using an ML-powered serverless stack

AWS Machine Learning

Prerequisites Complete the following prerequisite steps: If you’re a first-time user of QuickSight in your AWS account, sign up for QuickSight. Get insights from Amazon Kendra search metrics We can get the metrics data from Amazon Kendra using the GetSnapshots API. Please try out the solution and let us know your feedback.

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Auto-labeling module for deep learning-based Advanced Driver Assistance Systems on AWS

AWS Machine Learning

Then, using the SageMaker API, we can start the asynchronous inference job as follows: import glob import time max_images = 10 input_locations,output_locations, = [], [] for i, file in enumerate(glob.glob("data/processedimages/*.png")): A list of models is available in the models_manifest.json file provided by JumpStart.

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Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

AWS Machine Learning

We demonstrate CDE using simple examples and provide a step-by-step guide for you to experience CDE in an Amazon Kendra index in your own AWS account. After ingestion, images can be searched via the Amazon Kendra search console, API, or SDK. However, we can use CDE for a wider range of use cases.

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Best prompting practices for using Meta Llama 3 with Amazon SageMaker JumpStart

AWS Machine Learning

Prerequisites To try out this solution using SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all your AWS resources. Meta Llama 3 inference parameters For Meta Llama 3, the Messages API allows you to interact with the model in a conversational way.