Tags: llm* + production engineering* + observability*

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  1. SHREC is a physics-based unsupervised learning framework that reconstructs unobserved causal drivers from complex time series data. This new approach addresses the limitations of contemporary techniques, such as noise susceptibility and high computational cost, by using recurrence structures and topological embeddings. The successful application of SHREC on diverse datasets highlights its wide applicability and reliability in fields like biology, physics, and engineering, improving the accuracy of causal driver reconstruction.
  2. The article discusses the future of observability in 2025, highlighting the significant role of OpenTelemetry and AI in improving observability and reducing costs.
  3. New Relic's Nic Benders discusses the importance of the Innovation Centre in Hyderabad, their vision for AI, the benefits of their technologies for Indian digital businesses, and more.
  4. Infrastructure observability companies such as New Relic, Datadog, Dynatrace, Elastic and Splunk are actively enhancing their platforms through the integration of LLMs.
  5. With all the hype around AI/ML in observability, it's more likely than ever that companies benefit from storing and viewing data in one system and training ML models in another.

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