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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

address these limitations and brings new monitoring and analytical capabilities that weren’t available to Extensions 1.0: address these limitations and brings new monitoring and analytical capabilities that weren’t available to Extensions 1.0: available, and more are in the pipeline. Extensions 2.0 Extensions 2.0

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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

These integrations are implemented through Metaflow’s extension mechanism which is publicly available but subject to change, and hence not a part of Metaflow’s stable API yet. In addition to Spark, we want to support last-mile data processing in Python, addressing use cases such as feature transformations, batch inference, and training.

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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

This blog post explores how AI observability enables organizations to predict and control costs, performance, and data reliability. Augmenting LLM input in this way reduces apparent knowledge gaps in the training data and limits AI hallucinations. RAG augments user prompts with relevant data retrieved from outside the LLM.

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Detecting Speech and Music in Audio Content

The Netflix TechBlog

Practical use cases for speech & music activity Audio dataset preparation Speech & music activity is an important preprocessing step to prepare corpora for training. Content, genre and languages Instead of augmenting or synthesizing training data, we sample the large scale data available in the Netflix catalog with noisy labels.

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Measuring the importance of data quality to causal AI success

Dynatrace

While this approach can be effective if the model is trained with a large amount of data, even in the best-case scenarios, it amounts to an informed guess, rather than a certainty. Because IT systems change often, AI models trained only on historical data struggle to diagnose novel events. That’s where causal AI can help.

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Performetriks Training: Developing with Performance in Mind

Dotcom-Montior

This is the first online performance engineering training for developers, architects, QA, and test engineers. The Performetriks team will share their collective experience from real projects and educate your team on how to write high performance applications. Front-end Development for Performance. Testing for Performance.

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Enhanced AI model observability with Dynatrace and Traceloop OpenLLMetry

Dynatrace

AI model observability plays a crucial role in achieving this by addressing these key aspects: Model performance and reliability: Evaluating the model’s ability to provide accurate and timely responses, ensuring stability, and assessing domain-specific semantic accuracy. Maintained under the Apache 2.0