Remove APIs Remove Benchmark Remove Feedback Remove Metrics
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Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

AWS Machine Learning

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.

APIs 97
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Build well-architected IDP solutions with a custom lens – Part 4: Performance efficiency

AWS Machine Learning

You can save time, money, and labor by implementing classifications in your workflow, and documents go to downstream applications and APIs based on document type. This helps you avoid throttling limits on API calls due to polling the Get* APIs. Model monitoring The performance of ML models is monitored for degradation over time.

APIs 95
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Testing times: testingRTC is the smart, synchronized, real-world scenario WebRTC testing solution for the times we live in.

Spearline

testingRTC creates faster feedback loops from development to testing. Consequently, no other testing solution can provide the range and depth of testing metrics and analytics. And testingRTC offers multiple ways to export these metrics, from direct collection from webhooks, to downloading results in CSV format using the REST API.

Scripts 98
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New Product Releases for 2018

Spearline

An API (Application Programming Interface) will enhance your utilisation of our platform. Our RESTful API provides your developers with the ability to create campaigns, add numbers, time groups, export data for every test run, every day, every hour, every minute if that’s what you need to put your arms around your business.

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7-Point Audit Checklist for Customer Success Software

ChurnZero

To learn how ChurnZero helps customers comply with privacy laws, check out our documentation on ChurnZero’s API for privacy laws compliance. Collect user feedback on your Customer Success software For your Customer Success software to deliver results, your team needs to use it – and preferably enjoy using it if you want long-term success.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

In this scenario, the generative AI application, designed by the consumer, must interact with the fine-tuner backend via APIs to deliver this functionality to the end-users. Some models may be trained on diverse text datasets like internet data, coding scripts, instructions, or human feedback. 15K available FM reference Step 1.

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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning

The real-world performance and feedback are eventually used for further model improvements with full automation of the model training and deployment. Enable a data science team to manage a family of classic ML models for benchmarking statistics across multiple medical units. Once deployed, model performance is continuously monitored.