From Data Science to Production - deploy, scale, enjoy!
summary
Walks through taking data applications (R, Python, Scala examples) from development into production by Dockerizing apps, implementing CI/CD with commit-triggered deployments, and using immutable infrastructure and infrastructure-as-code on AWS for rolling deploys. Also explores serverless architectures for data workloads, showing an auto-scaling outlier-detection example and discussing when to apply these deployment and scaling patterns.
AI-written summary · about
related talks
46mDev Ops meets Data Science Taking models from prototype to production with Docker
30mDevOps For Data Teams
29mData Science in Production
42mData Science Meets Containers
35mScaling up to Big Data Devops for Data Science
14mRobust R Deployments: Building a Pipeline from RStudio to Production
01h 02mDocker for Data Science: Reproducibility and Deployment
30mA Devops Approach to Data: A Tale Through the Trenches in Search of a Cloud Data Revolution