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Mitigating Bias in AI Through Continuous Monitoring and Validation

DZone

AI models often mirror the data on which they are trained. To overcome this issue, continuous monitoring and validation emerge as critical processes which are essential for ensuring that AI models function ethically and impartially over time. It can unintentionally include existing societal biases, leading to unfair outcomes.

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Getting Hands-on Training into more hands in 2021

Dynatrace

And we know as well as anyone: the need for fast transformations drives amazing flexibility and innovation, which is why we took Perform Hands-on Training (HOT) virtual for 2021. Taking training sessions online this year lets us provide more instructor-led sessions over more days and times than ever before.

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Best Practices for Setting up Monitoring Operations for Your AI Team

DZone

There is often a lack of discussion around the operations needed for machine learning (ML) in production and monitoring specifically. ML teams have traditionally been research-oriented, focusing heavily on training models to achieve high testing scores.

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Best Practices in Cloud Security Monitoring

Scalegrid

Cloud security monitoring is key—identifying threats in real-time and mitigating risks before they escalate. The automatic nature also allows for quick response times in addressing any identified security concerns making it an ideal solution for effective cloud security monitoring.

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Unmatched scalability and security of Dynatrace extensions now available for all supported technologies: 7 reasons to migrate your JMX and Python plugins

Dynatrace

already address SNMP, WMI, SQL databases, and Prometheus technologies, serving the monitoring needs of hundreds of Dynatrace customers. JMX monitoring extensions are currently being migrated. Extensions can monitor virtually any type of technology in your environment. and focusing on a much-improved version 2.0 Extensions 2.0

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How to Monitor for Data and Concept Drift

DZone

Data Drift Data and concept drift are frequently mentioned in ML monitoring, but what exactly are they, and how are they detected? Furthermore, given the common misconceptions, are data and concept drift things to be avoided at all costs or natural and acceptable consequences of training models in production? Read on to find out.

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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

Augmenting LLM input in this way reduces apparent knowledge gaps in the training data and limits AI hallucinations. The LLM then synthesizes the retrieved data with the augmented prompt and its internal training data to create a response that can be sent back to the user. million AI server units annually by 2027, consuming 75.4+

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