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Let’s talk about Chat GPT in the Contact Center

CCNG

Initially Fred helps define how Chat GPT will be useful using API's along with Adam's caution of betting on operational efficiency and accuracy too quickly - “I thought the most fascinating part for me was some members shared they have used ChatGPT for 1 to 1 functions but none has started using it commercially.

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How to Create an Interactive Scavenger Hunt with Nexmo’s SMS and Voice API

Nexmo

And thus I thought it’d be fun to design and build something with Nexmo’s Voice and SMS APIs to do just that. Event URL text field enter your Glitch URL: [link] Glitch URL].glitch.me/events. Replace the API Key, API Secret, App ID, and your Nexmo Number. app.post('/events', (req, res) => { res.status(200).send();

APIs 63
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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Continuous integration and continuous delivery (CI/CD) pipeline – Using the customer’s GitHub repository enabled code versioning and automated scripts to launch pipeline deployment whenever new versions of the code are committed. When defined events occur, EventBridge can invoke a pipeline to run in response.

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Using Web Components in a React Application

Nexmo

Vonage API Account. To complete this tutorial, you will need a Vonage API account. Once you have an account, you can find your API Key and API Secret at the top of the Vonage API Dashboard. The Web Component can also emit the phone number of a contact to the application as a custom event when clicked.

APIs 118
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Testing times: testingRTC is the smart, synchronized, real-world scenario WebRTC testing solution for the times we live in.

Spearline

And testingRTC offers multiple ways to export these metrics, from direct collection from webhooks, to downloading results in CSV format using the REST API. Flip the script With testingRTC, you only need to write scripts once, you can then run them multiple times and scale them up or down as you see fit. Happy days!

Scripts 98
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Build an image search engine with Amazon Kendra and Amazon Rekognition

AWS Machine Learning

Therefore, users without ML expertise can enjoy the benefits of a custom labels model through an API call, because a significant amount of overhead is reduced. A Python script is used to aid in the process of uploading the datasets and generating the manifest file. then((response) => { resolve(Buffer.from(response.data, "binary").toString("base64"));

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Move Amazon SageMaker Autopilot ML models from experimentation to production using Amazon SageMaker Pipelines

AWS Machine Learning

Autopilot training jobs start their own dedicated SageMaker backend processes, and dedicated SageMaker API calls are required to start new training jobs, monitor training job statuses, and invoke trained Autopilot models. We use a Lambda step because the API call to Autopilot is lightweight. script creates an Autopilot job.

Scripts 76