Harnessing Microservices for Scalable, Fault-Tolerant ML Systems
summary
Claims microservices let inference, training and APIs scale and fail independently and give language freedom while costing data consistency, network overhead and operational complexity. Concrete tools named include gRPC HTTP2 for fast RPC, event streaming and message queues for decoupling, containers and Kubernetes + Prometheus + Grafana for orchestration and observability, serverless (AWS Lambda Google Cloud Functions) and ML platforms KS serve SageMaker, and tracing with Jaeger or Zipkin. Practical guidance: serverless first, pick one easy high-impact service, decompose modestly, measure and add tracing and centralized logs.
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chapters (9)
- 0:00Introduction to Microservices in ML Ecosystems
- 0:16Challenges of Monolithic Architectures
- 1:23Benefits of Microservices
- 4:32Challenges of Microservices
- 6:30Communication in Microservices
- 9:24Deployment Approaches for ML
- 12:25Observability and Monitoring
- 15:08Emerging Trends and Takeaways
- 21:06Conclusion
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