Remove platform tester-automation
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Get Ready to Shift CX into High Gear with Cyara Call Explorer

Cyara

Today marks a momentous occasion in the world of test automation. We are excited to announce the official launch of Cyara Call Explorer, a brand new feature included across the Cyara Automated CX Assurance Platform.

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Technographics: Your Shortcut to Improving Interoperability

Centercode

In the context of customer testing, this type of segmentation lets you select testers based on the details of their unique tech ecosystems. Without technographics, the story ends there; you now have to begin the exhausting process of following up with hundreds of testers to figure out common causes. Write it down.

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How to Increase Your Beta Engagement by 75%

Centercode

Begging for feedback from unenthusiastic or inexperienced testers is a frustratingly common occurrence for many beta and delta testing teams. But tester engagement — and the resulting feedback — are both vital to test success. Check out our free ebook, the Tester Engagement Pocket Map ! Interested? Centercode with the Assist.

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4 Types of Feedback You’re Not Collecting (But Should Be!)

Centercode

With a targeted group of candidates and the right setup in place , your testers will be more than willing to share their thoughts about your product. But the way testers want to share their input may not be limited to weekly surveys or the odd bug. Tester praise is great for your engineering and UX teams. Discussion Boards.

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Ask the Expert: Should I Add More Testers?

Centercode

Is more testers always the best solution when you’re experiencing a dip in feedback? In this Ask the Expert series, Centercode’s Chris Rader weighs in on large tester teams: when they work, when they don’t, and how to manage when your stakeholders want to keep adding testers. What should I do? Chris Rader.

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Delivering Picture-Perfect Experiences in the Year of Streaming

Centercode

To fill the gaps, Centercode uses strategic tester recruitment and test platforms. Reliable load testing is critical in this regard and hinges on two vital (and honestly pretty daunting) factors: recruiting a large enough number of testers and getting them all to participate in the same place at the same time. Picture Quality.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

After the completion of the research phase, the data scientists need to collaborate with ML engineers to create automations for building (ML pipelines) and deploying models into production using CI/CD pipelines. All the produced models and code automation are stored in a centralized tooling account using the capability of a model registry.

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