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DevOps 2.0: Applying Machine Learning in the CI/CD Chain


Explore how ML can be implemented in your organization, so you can (for example) enable the automated assessment of test results for far more complex criteria, such as defining thresholds based on statistical significance rather than just presence/absence of specific criteria.



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The ability to predict defects, failures and trends is what makes Machine Learning (ML) of particular interest to DevOps teams. ML-powered tools are emerging as a key way to make DevOps more efficient and empowered, and there’s no role in software that can benefit from greater efficiency than DevOps.

Explore how ML can be implemented in your organization, so you can (for example) enable the automated assessment of test results for far more complex criteria, such as defining thresholds based on statistical significance rather than just presence/absence of specific criteria. All without slowing down the SDLC.

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