Uber MLOps Production Pipeline
End-to-end MLOps pipeline: automated training, deployment, monitoring, and continuous delivery for production ML systems.
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Designing an end-to-end MLOps production pipeline with automated training, deployment, monitoring, and continuous delivery for enterprise-scale machine learning systems
🤖 100+ Production Models🚀 Automated CI/CD📊 Real-time Monitoring⚡ 1M+ Predictions/sec
✅ Functional Requirements
- • Automated model training triggered by data changes or schedules
- • Continuous integration/deployment for ML models with A/B testing
- • Feature engineering pipelines with data validation and monitoring
- • Model versioning, lineage tracking, and rollback capabilities
- • Real-time and batch inference serving with auto-scaling
- • Data drift detection and model performance monitoring
- • Automated retraining when performance degrades
- • Multi-environment deployment (dev, staging, production)
⚡ Non-Functional Requirements
- • Handle 1M+ predictions per second with <100ms P99 latency
- • Support 100+ models in production simultaneously
- • 99.9% uptime for critical prediction services
- • Data processing throughput of 10TB+ per day
- • Model training completion within 4 hours for daily retrain
- • Feature freshness <5 minutes for real-time features
- • Full audit trail and compliance (SOX, GDPR)
- • Cost optimization with automatic resource scaling
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