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Uber's Michelangelo: Strategic AI Overhaul and Impact // #239

MLOps.community

NOTE

Quality Considerations in ML Lifecycle

When creating Michelangelo 2.0, quality considerations were divided into phases of the ML lifecycle. During training, factors such as code review, test coverage, reliability, and hardware costs were important. For the model itself, considerations included accuracy, freshness, and reproducibility for model serving. In the serving phase, metrics like latency, availability, and cost were crucial. A framework called MES (Model Excellence Score) was created for measuring and monitoring key metrics.

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