Posts tagged: monitoring
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Operate what you build: lessons for small AI products
Analyzing the ownership mindset in production engineering and how to apply it to AI workflows, CRMs, dashboards, and small products.
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RecSysOps: operating a recommender system after deployment
Notes from Netflix RecSysOps on operating recommender systems: issue detection, issue prediction, diagnosis, and resolution when a recommendation system goes into production.
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Monitoring AI workflows: learning from Netflix but applying at small scale
Designing just enough monitoring for small AI workflows: logs, metrics, traces, evaluation, and business signals.
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Microservice Architecture Patterns for Scalable ML Systems
Practical notes on how to break a Machine Learning system into smaller services to make it easier to deploy, monitor, and scale.
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Netflix Microservices: why is observability more important than you think?
Reading Netflix's post on microservices to understand why distributed systems need multi-level observation: request flows, bottlenecks, and instance-level metrics.
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What blocks does an ML platform need? Learning from Uber Michelangelo
Analyzing Uber Michelangelo to understand that a production ML platform needs data, training, deployment, prediction, and monitoring.
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