Designing ML Systems
Architect ML for production.
Training Pipeline
Data collection. Feature engineering. Model training. Evaluation. Registry.
Inference Pipeline
Feature serving. Model serving. Monitoring. A/B testing.
Reliability
Retries. Circuit breakers. Failover. Monitoring.
Key Takeaways
- Separate training and inference
- Monitor for issues
- Design for reliability