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QH036 How can you test machine learning?

36:00
 
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Manage episode 363985183 series 2306674
内容由QualityMinds GmbH提供。所有播客内容(包括剧集、图形和播客描述)均由 QualityMinds GmbH 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Friends of Quality, welcome to our 36th podcast episode! Our guests Namrata and Michael discuss various methods of testing machine learning, dealing with flaky tests and AI bias, meta-learning and much, much more! We are looking forward to your feedback - enjoy!

Speakers:

Namrata Gurung:

namrata.gurung@qualityminds.de

https://www.namratagurung.com/

https://www.linkedin.com/in/namratagurung/

https://qualityminds.com/de/meet-the-dev-namrata

Michael Mlynarski:

https://www.linkedin.com/in/michael-mlynarski-12645024/

https://www.xing.com/profile/Michael_Mlynarski/cv

https://twitter.com/MichalMM

Resources:

QualityHeroes podcast episode on machine learning:

https://qualityminds.com/de/qualityheroes-podcast-folge-28-what-is-machine-learning-and-why-should-we-have-a-closer-look-at-it/

QualityMinds page dedicated to machine learning and AI testing:

https://qualityminds.com/services/specialized-qa-services-and-concepts/ai-ml-testing/

ISTQB AI testing:

https://www.istqb.org/certifications/artificial-inteligence-tester

Machine learning solution for flaky tests:

https://medium.com/fitbit-tech-blog/a-machine-learning-solution-for-detecting-and-mitigating-flaky-tests-c5626ca7e853

Meta-learning:

https://medium.com/fitbit-tech-blog/a-machine-learning-solution-for-detecting-and-mitigating-flaky-tests-c5626ca7e853

Talk on AI Bias and Fairness:

https://youtu.be/wmyVODy_WD8

Drift in machine learning:

https://superwise.ai/blog/everything-you-need-to-know-about-drift-in-machine-learning/

Adversarial attacks:

https://viso.ai/deep-learning/adversarial-machine-learning/

About QualityMinds:

www.qualityminds.com/de

https://www.linkedin.com/company/qualityminds-gmbh

https://twitter.com/QualityMindsDE

Feedback, questions and topic requests:

podcast@qualityminds.de

  continue reading

43集单集

Artwork
icon分享
 
Manage episode 363985183 series 2306674
内容由QualityMinds GmbH提供。所有播客内容(包括剧集、图形和播客描述)均由 QualityMinds GmbH 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Friends of Quality, welcome to our 36th podcast episode! Our guests Namrata and Michael discuss various methods of testing machine learning, dealing with flaky tests and AI bias, meta-learning and much, much more! We are looking forward to your feedback - enjoy!

Speakers:

Namrata Gurung:

namrata.gurung@qualityminds.de

https://www.namratagurung.com/

https://www.linkedin.com/in/namratagurung/

https://qualityminds.com/de/meet-the-dev-namrata

Michael Mlynarski:

https://www.linkedin.com/in/michael-mlynarski-12645024/

https://www.xing.com/profile/Michael_Mlynarski/cv

https://twitter.com/MichalMM

Resources:

QualityHeroes podcast episode on machine learning:

https://qualityminds.com/de/qualityheroes-podcast-folge-28-what-is-machine-learning-and-why-should-we-have-a-closer-look-at-it/

QualityMinds page dedicated to machine learning and AI testing:

https://qualityminds.com/services/specialized-qa-services-and-concepts/ai-ml-testing/

ISTQB AI testing:

https://www.istqb.org/certifications/artificial-inteligence-tester

Machine learning solution for flaky tests:

https://medium.com/fitbit-tech-blog/a-machine-learning-solution-for-detecting-and-mitigating-flaky-tests-c5626ca7e853

Meta-learning:

https://medium.com/fitbit-tech-blog/a-machine-learning-solution-for-detecting-and-mitigating-flaky-tests-c5626ca7e853

Talk on AI Bias and Fairness:

https://youtu.be/wmyVODy_WD8

Drift in machine learning:

https://superwise.ai/blog/everything-you-need-to-know-about-drift-in-machine-learning/

Adversarial attacks:

https://viso.ai/deep-learning/adversarial-machine-learning/

About QualityMinds:

www.qualityminds.com/de

https://www.linkedin.com/company/qualityminds-gmbh

https://twitter.com/QualityMindsDE

Feedback, questions and topic requests:

podcast@qualityminds.de

  continue reading

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