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Authors: Amit Kalamkar, Vigith Maurice
2023-04-21

tldr - powered by Generative AI

The presentation discusses the importance of data curation and integration in understanding anomalies in a system. It also highlights the architecture of an operational data platform and the use of a new project called Pneuma for stream processing and analytics.
  • Data curation and integration are crucial in understanding anomalies in a system
  • An out-of-the-box price analytics feature is available for slicing and dicing information
  • Access logs are provided for developers to guide them in debugging
  • The architecture of an operational data platform is discussed, which collects information from multiple layers
  • Pneuma is a new project for stream processing and analytics, which is language agnostic and easy to use
  • The front-end design is based on microservices and auto-instrumentation
  • Streaming AOPS is done by streaming data providers
Authors: Amit Kalamkar, Vigith Maurice
2022-10-27

tldr - powered by Generative AI

Intuit's new platform, NewMapRaj, uses AI-based observability to improve change-related incidents and reduce MTTR and MTTD.
  • NewMapRaj is a Kubernetes native data processing and analytics tool used to derive actionable insights for different areas like operational excellence, cost, and security.
  • Intuit's core principle is innovation, and they invest in Argo to make sure their products are always available and issues are resolved quickly.
  • Change-related incidents were causing one-third of Intuit's incidents, and their MTTR was higher due to disjointed deployment and operational experiences.
  • NewMapRaj integrated AI-based observability into Argo CD and rollouts to add a metrics tab, run a multivariant model, and remove humans from the equation.
  • The AI-based observability is computed in real-time and normalized to a human understandable format.
  • NewMapRaj uses a streaming system that does feature engineering and inferencing, and triggers inline training to discover new applications and configurations.
  • The challenges of real-time streaming include boilerplate code and non-standard code, making it difficult to do quick experimentation and extension.