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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: What’s available now and what’s coming later We’ve already started to migrate Dynatrace-developed Extensions 1.0 available, and more are in the pipeline. Extensions 2.0 to the Extension Framework 2.0.

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The Ultimate Guide to Database High Availability

Percona

To make data count and to ensure cloud computing is unabated, companies and organizations must have highly available databases. This guide provides an overview of what high availability means, the components involved, how to measure high availability, and how to achieve it. How does high availability work?

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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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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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Training is expensive (Not)!

Allen Holub

I’m often contacted by someone who says: We could really use your training (or consulting services). The first answer is that I provide some of the highest quality training and consulting available, and there are plenty of examples of my work… The post Training is expensive (Not)! Do you have any tips?

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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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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.