Production Deployment: Docker, APIs, and Scheduling

Moving a working notebook into a scheduled, monitored service

Production Deployment: Docker, APIs, and Scheduling
Expert MLOps & Pipelines 1 lessons Samuel Appiah Kubi

About this course

A pipeline that only exists as a notebook you run by hand isn't a monitoring system — it's a demo. This course covers what closes that gap: containerising a pygeovision pipeline properly, exposing it as a REST API, scheduling it to run automatically, and handling secrets the way a real deployment requires, all sized for resource-constrained infrastructure rather than assuming a data-centre budget.

What you'll learn

  • Write a production-appropriate Dockerfile for a pygeovision pipeline
  • Build a FastAPI endpoint for search, download and inference
  • Schedule recurring monitoring with cron or Celery and Redis
  • Deploy to a modest cloud VM rather than assuming unlimited infrastructure
  • Handle credentials securely instead of hardcoding them

Requirements

  • Advanced pygeovision (custom training recommended)
  • Basic Linux command line
  • Docker installed

Course content

A Production Dockerfile, Explained Line by Line Preview 26 min
Free
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  • LevelExpert
  • Lessons1
  • CertificateYes
  • AccessLifetime

Samuel Appiah Kubi Instructor