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Telemetry with monitoring and observability to cloud native infrastructure and workloads is the essential foundation for cloud services, and those telemetry data collected is mostly in time series formulation. It's often low efficient due to high dimension and high volume of tremendous historical/real-time telemetry data with large-scale cloud infrastructure and distributed microservices to be processed by traditional methods.This session will review the current status and challenges of traditional ways to manage/operate the cloud infrastructure and microservices, and comparingly introducing AI on those telemetry data with transformation and ML algorithms to enable more intelligence on cloud infrastructure/workloads management and operation, such as anomaly detection, resource forecasting, failure detection, root-cause analysis and so on. Specifically, using the Chronos to pave the way to land the AIOps to cloud infrastructure/services.
