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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 135
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Simplify automotive damage processing with Amazon Bedrock and vector databases

AWS Machine Learning

In the automotive industry, the ability to efficiently assess and address vehicle damage is crucial for efficient operations, customer satisfaction, and cost management. By combining these powerful tools, we have developed a comprehensive solution that streamlines the process of identifying and categorizing automotive damage.

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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 135
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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 91
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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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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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Your guide to generative AI and ML at AWS re:Invent 2023

AWS Machine Learning

In this innovation talk, hear how the largest industries, from healthcare and financial services to automotive and media and entertainment, are using generative AI to drive outcomes for their customers. This session uses the Claude 2 LLM as an example of how prompt engineering helps to solve complex customer use cases. Reserve your seat now!