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Learn how to apply Site Reliability Engineering principles to build resilient streaming AI platforms that automatically detect and combat model drift in this 19-minute conference talk. Discover how to transform model drift from a reactive data science problem into a proactive operational challenge through robust platform engineering. Explore real-world implementation strategies for monitoring systems that detect drift in real-time, automated remediation workflows that respond without human intervention, and continuous validation pipelines that maintain model reliability as data distributions evolve. Master the use of Kafka for scalable streaming data architecture, implement end-to-end observability with Prometheus and Grafana for comprehensive model performance visibility, and understand how to treat machine learning models as production services requiring the same reliability standards as critical infrastructure. Gain insights into balancing automation with human oversight in SRE practices and learn practical approaches to maintaining AI model accuracy and resilience in production streaming environments where data patterns continuously change.
