<ul><li>Site Reliability Engineers (SREs) responsible for ML systems need to focus on pipeline management for reliable services.</li><li>Training ML models involves managing data ingestion, freshness, training, and deployment efficiency.</li><li>ML systems' challenges include capacity planning, resource management, and cost understanding.</li><li>Data freshness is crucial for ML systems' health and user experience, with varying requirements by product.</li><li>Automation, SLOs, and understanding data volume are key for reliable ML pipelines.</li><li>Efficient machine learning inference is vital for real-world deployment, optimizing cost effectiveness.</li><li>Knowing how to efficiently use specialized hardware is essential for cost-effective AI pipelines.</li><li>Automation and minimizing manual effort are crucial for building resilient data pipelines.</li><li>Successful ML deployments require holistic management, including data pipelines, training, and monitoring.</li><li>Using GKE for AI orchestration can further enhance ML system management and optimization.</li></ul>

An SRE’s guide to optimizing ML systems with MLOps pipelines

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