Udemy

Serverless Docker-based Python Application on Google Cloud

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  • 21,730 Students
  • Updated 2/2023
4.4
(511 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
4 Hour(s) 6 Minute(s)
Language
English
Taught by
Justin Mitchel
Rating
4.4
(511 Ratings)
3 views

Course Overview

Serverless Docker-based Python Application on Google Cloud

Learn to Build & Deploy a Python Web Application using Docker, Cloud Build & Cloud Run on the Google Cloud Platform

Deploy a Serverless Python Application with Docker Containers and Google Cloud Run.

Running our apps on managed serverless architectures allows us to focus on our code and deploy more without worrying about the underlying infrastructure. What's better, our serverless applications only cost us money when they're used this is because serverless apps can scale to 0 running instances or scale up as needed.

Serverless is great for testing all kinds of app ideas as well as testing various stages of a stable app. Cloud Run is a managed service that unlocks serverless apps for your projects on Google Cloud. It's how we run this website exactly.

Cloud Run is a managed Knative offering that runs on Kubernetes so if you're interested in deploying a self-managed Knative service, consider watching my course Try Knative.

Here's what we're going to learn in this course:

  • Creating a basic FastAPI web app (in Python)

  • Implement python-decouple to manage environment variables in FastAPI

  • Writing a basic automated test to ensure our app is working as needed

  • Prepare and learn about a Dockerfile before building a container

  • Building a Docker container locally

  • Running a custom Docker image (container) locally

  • Push our code to GitHub

  • Leverage GitHub Actions to perform CI/CD workflows

  • Using Github Actions to build and push our Docker container to Google Cloud

  • Use Google Cloud Secrets manager within our Python app

  • Update Secrets in Github Actions as needed

  • Deploy our app continuously on Google Cloud Run

Course was completely revamped and released on Feb 15th, 2023

Course Content

  • 9 section(s)
  • 38 lecture(s)
  • Section 1 Welcome
  • Section 2 Installations
  • Section 3 Python Project Setup
  • Section 4 Docker and Containerizing Python Apps
  • Section 5 Containers & Google Cloud
  • Section 6 CI/CD with GitHub & GitHub Actions
  • Section 7 Google Cloud Secrets Manager
  • Section 8 Wrap up section
  • Section 9 Archived Lessons: 2020 Version of this Course

What You’ll Learn

  • Deploying a Serverless Docker-based Python Web Application
  • Setup the Google Cloud Command Line tool on your system
  • Build and run Docker containers Locally
  • Web Application Deployment to Google Cloud
  • Learn what Serverless Apps app
  • Deploy to Cloud Run on Google Cloud Platform
  • Build a Docker Container for Python apps
  • Learn and understand Docker Containers and Why they're useful
  • Learn CI/CD Practices with GitHub
  • Leverage GitHub Actions to automate testing, building, and pushing containers
  • Use GitHub Actions workflows to manage Google Cloud projects
  • Implement Google Secret manager and use GitHub as our single source of truth
  • Learn various Docker and Dockerfile Debugging Techniques.

Reviews

  • R
    Rogersentongo
    2.0

    The course curriculum is good. But The presentation style, pacing and explanation is terrible. I will never buy a course from this tutor again. He inputs commands in terminal without properly explaining what he's doing. The pacing is completely off. Imagine I was on a Mac like him and had to stop read the docs to understand exactly what he's doing. Wasted my money on this one.

  • B
    Bojan Karaica
    5.0

    Really concise and to the point. Organized and well structured expectations

  • K
    Khemraj Suryakant Sawantmorye
    5.0

    Fantastic

  • I
    Igor Chebuniaev
    4.0

    I like the content, but the instructor seem to rush a lot. A lot of time is dedicated to managing secrets, but the application example being used is a bit remote from real life. I still don't understand why I would need gcloud secrets on my ML project.

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