DevOps for Data Science

• Use CI/CD ➡️ Code Promotion and Integration Processes
  • Structure output so moving to prod or updating is easy.
• Infrastructure as Code ➡️ Manage Environments as Code
  • Reproducible & secure environments are… reproducible and secure!
• Microservices ➡️ Data Science Project Components
  • Figure out how to subdivide things to make updating less painful.
• Monitoring & Logging ➡️ Monitoring & Logging
  • Data science doesn’t do enough of this, but he’ll tell us how we should!
• Other Things ➡️ Other Things 🙃
  • Chapter about Docker for Data Science here, because it deserves its own chapter.
  • Section 2 will be all about things like communication, collaboration, and review practices.