Robust R Deployments: Building a Pipeline from RStudio to Production
summary
R code serves a machine learning microservice behind a Plumber API wrapped in Docker and built by GitHub Actions CI. CI runs unit, integration and end‑to‑end tests; successful containers progress through three environments—staging, release, production—where end‑to‑end checks run before deploy. Production runs on Kubernetes with a ReplicaSet of three replicas, a load balancer routing requests from a JavaScript frontend and Scala backend to redundant R instances.
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chapters (17)
- 0:00Introduction
- 0:43What I work on
- 2:13What is an R microservice
- 3:02DVO1 Microservice Wild
- 3:41Data Scientist vs Software Engineer
- 4:01Three DevOps Principles
- 4:36Continuous Integration Workflow
- 5:36CI Build
- 6:35Automated Tests
- 7:04Unit Tests
- 8:29Integration Tests
- 8:52EndtoEnd Tests
- 9:17Writing Tests
- 9:59Continuous Deployment
- 11:20Cluster Computing
- 12:17Replica Sets
- 13:02Conclusion
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