Accountability APIs Automotive 
AWS Machine Learning
NOVEMBER 7, 2024
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.
AWS Machine Learning
MARCH 19, 2025
Note that these APIs use objects as namespaces, alleviating the need for explicit imports. API Gateway supports multiple mechanisms for controlling and managing access to an API. AWS Lambda handles the REST API integration, processing the requests and invoking the appropriate AWS services.
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AWS Machine Learning
MARCH 27, 2025
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",
AWS Machine Learning
APRIL 30, 2025
For instance, Iveco Group , a global automotive leader active in the Commercial and Specialty Vehicles, Powertrain, adopted a structured cloud-operating model, leveraging IaC via Terraform for consistent and repeatable deployments across environments.
AWS Machine Learning
MAY 1, 2024
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.
AWS Machine Learning
NOVEMBER 21, 2022
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.
AWS Machine Learning
JULY 3, 2023
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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