Python in Containers from Udemy

Docker and Kubernetes are the Must-Have Skills for Python Enginner these days.

Whether your focus is in Machine Learning & Data Science, or you use Python as General Programming Language, you must understand Docker & Kubernetes. Both form a basis of Modern Cloud Native Applications built in Microservices Architecture.

Quotes from selected course reviews:

"It covers pretty much everything you'd expect from enterprise project" [email protected]
"This course is absolute gold for data science and machine learning people because all Docker and Kubernetes courses out there focus on nothing but web applications. Thanks to the instructor for handling the concept of virtualization from a much needed different perspective. There are a lot of sources for learning ML and DS but skills taught in this course are what will make you stand out from the crowd." Mertkan Alacahan
"Spot on. Great depth yet very concise." Toby Patterson
"This is a deep deep deep dive in Docker with python. It is the complete course. Thanks for putting this together it is more than enough for what a need. I think watching the basic lectures and some selected topics I get what I needed and this became my docker reference guide if I need to solve a specific scenario. Thanks for putting this together. Highly recommend the course if you are a python developer." Pedro

In this Course you learn how to:

Develop and Explore Machine Learning & Data Science Jupyter Notebooks in Docker
Run Machine Learning Models in Production with Kubernetes and Docker Swarm
package your Python Code into Containers
publish your Containers in Image Registries
deploy Containers in Production
build highly modular Container-based Services in Micro-Services fashion
monitor and maintain Containerized Apps

You are going to become fluent and confident in using Docker Tools to create top-class Containers running your Python Code. You master Docker Runtime Tools like Compose and Swarm to run them. The Course also gives you sound knowledge and deep understanding of Kubernetes as the Application Platform. You gain confidence in Designing your Application to run on Kubernetes, as well as get deep knowledge of writing Kubernetes Object Declarations.

The Course is full of practical Exercises. There are over 40 GitHub Repositories full of Code Samples for the Course.

You can use the Course in two ways:

If you use Python for Machine Learning & Data Science, go Top-Down: start with Section 7 to quickly gain practical Docker skills and use Sections 2 to 6 to dig deeper into specific Container Topics.
If you want to use Python for Web Apps & Microservices, try Bottom-Up: use the Course in linear manner.

Start building Containers today.

What's inside

Learning objectives

Build container image with python application in it
Ship container images to docker hub and other container image registries
Run jupyter notebooks in docker
Use docker desktop for windows pro and macos
Use docker toolbox for windows home
Use docker machine to create virtual machines with docker software
Master dockerfile to automate container image build
Create custom container images from scratch
Use python official images
Design flask and django multi-container deployments
Automate multi-container deployments with docker compose

Containerize tensorflow models into microservices
Deploy complex, multi-container applications in docker swarm
Deploy complex, multi-container application in kubernetes
Use kubernetes with minikube on a development host
Use kubernetes in public cloud (using example of google kubernetes engine)
Kubernetes objects: pods, pod controllers: replicaset, deployment, job, cronjob, services, ingress, persistent volumes
Writing kubernetes object template files
Monitor and manage application in kubernetes
Execute containers with nvidia gpu acceleration
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Build container image with python application in it
Ship container images to docker hub and other container image registries
Run jupyter notebooks in docker
Use docker desktop for windows pro and macos
Use docker toolbox for windows home
Use docker machine to create virtual machines with docker software
Master dockerfile to automate container image build
Create custom container images from scratch
Use python official images
Design flask and django multi-container deployments
Automate multi-container deployments with docker compose
Containerize tensorflow models into microservices
Deploy complex, multi-container applications in docker swarm
Deploy complex, multi-container application in kubernetes
Use kubernetes with minikube on a development host
Use kubernetes in public cloud (using example of google kubernetes engine)
Kubernetes objects: pods, pod controllers: replicaset, deployment, job, cronjob, services, ingress, persistent volumes
Writing kubernetes object template files
Monitor and manage application in kubernetes
Execute containers with nvidia gpu acceleration
Show more
Show less

Syllabus

Introduction

This Lecture introduces Python in Containers Course. Course Agenda is discussed.

The Concept of Linux Containers

Containers Explained

Build, Ship, Run

Introduction to Docker

Ecosystem of Container Technologies

Introduction to Container Orchestration

Shipping the Image to Docker Hub

Running our App in Various Environments

Docker Deep Dive

Installing Docker for a Developer

Create Docker ID

Play with Docker

Install Docker on Ubuntu

Install Docker on CentOS

Docker on Linux - Security Warning

Docker Desktop on Windows Pro

Introduction to Windows Containers

Docker Desktop on MacOS

Docker Toolbox for Windows Home

Docker Desktop on Windows 10 Home with WSL2

Running Containers with Docker

Integrating Containers with a Host System

Container Images

Managing Containers

Running Multiple Containers

Container Networking

Data Persistency - Volumes

Dockerfile Introduction

Docker Hub Introduction

Python Base Images

Docker GUIs Part 1 - Kitematic

Docker GUIs Part 2 - Portainer

Docker Machine Overview

Docker Machine with VirtualBox

Docker Machine with Hyper-V

Docker Machine on AWS Cloud Hosts

Docker Machine on Google Cloud Hosts

Build Container Images

In this Lecture we look at all Components necessary to build a Container with Python Application.

In this Lecture we review a typical workflow of actions in lifecycle of Container with Python Application.

Key Design Principles of Containerized Python Application are reviewed in this Lecture.

Manual Image Build Process

Dockerfile - Automation of Image Build

Dockerfile Commands - Introduction and FROM

Dockerfile Commands - WORKDIR, COPY, ADD

Dockerfile Commands - RUN

Dockerfile Commands - ENV, LABEL, USER

Dockerfile Commands - VOLUME and EXPOSE

Dockerfile Commands - ENTRYPOINT and CMD

Parametrizing Dockerfiles with ARG

Building and Running Reusable Images

Build time versus Run time Execution

Building smaller Images

Multistage Image Build

Building Custom Python Images

Build Base Images from Scratch

Dockerizing PyTest and Pdb - Simple Case

Django Containerization for Development

Django Containerization for Production

Application Servers to Run Django and Flask

Production--grade Database Engine - PostgreSQL

Production--grade Database Engine - MariaDB

Implementing Proxy Server

The need of Automation

Ship Containers

Shipping Images

Image Registries and Repositories

Review of Key Cloud Registries

Review of Local Registry Technologies

GitHub and Docker Hub Integration

GitLab Container Image Build Workflow

Vulnerability Scanning of Images

Run Containers in Docker

Running Production Containers in Docker

Docker Compose - Introduction

Docker Compose File - Version and Volumes

Docker Compose File - Networks

Docker Compose File - Services

Managing Images with Docker Compose

Application Lifecycle with Docker Compose - Part 1

Application Lifecycle with Docker Compose - Part 2

Introduction to Docker Swarm

Provisioning Swarm with Docker Machine

Standalone Containers in Swarm

Services in Swarm

Service Modes and Ingress Routing Mesh

Application Stack in Swarm - Part 1

Application Stack in Swarm - Part 2

Application Environment in Swarm - Part 1

Application Environment in Swarm - Part 2

Application Lifecycle in Swarm

Summary of Docker Runtime Environment

Run Containers in Kubernetes

Introduction to Kubernetes

Helicopter View of Kubernetes as the Application Platform

Installing a Small Kubernetes Cluster

Running Simple Application in Minikube

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides robust and practical guidance on building, deploying, and managing Docker containers and Kubernetes applications specifically designed for Python engineers

Engages learners with real-world use cases and demonstrates the application of Docker and Kubernetes within a machine learning and data science context

Developed by industry expert Kris Celmer, ensuring the currency and relevance of the course content

Well-regarded within the Python community, garnering positive feedback from participants highlighting its practicality and applicability

Offers a comprehensive roadmap for learners, covering fundamental concepts to advanced Kubernetes deployment strategies

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Python in Containers with these activities:

Review Python Basics

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Refresh your knowledge of Python basics to strengthen your foundation for the course, ensuring a smooth transition into more advanced concepts.

Browse courses on Python

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Review online tutorials or documentation on Python basics.
Solve practice problems or coding challenges to reinforce your understanding.
Consider taking a refresher course or workshop on Python.

Read Docker Deep Dive by Nigel Poulton

Show steps

Gain a deeper understanding of Docker and its ecosystem to enhance your comprehension of the course material.

View Docker Deep Dive on Amazon

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Purchase or borrow a copy of 'Docker Deep Dive'.
Allocate dedicated time for reading, setting aside approximately two hours per session.
Take notes and highlight key concepts to aid in retention.
Engage in discussions with peers or online forums to clarify understanding.

Attend a Docker and Kubernetes Workshop

Show steps

Accelerate your learning by attending a hands-on workshop focused on Docker and Kubernetes, providing practical experience and expert guidance.

Browse courses on Docker

Show steps

Identify and register for a reputable Docker and Kubernetes workshop.
Prepare for the workshop by reviewing basic concepts.
Actively participate in the workshop, asking questions and taking notes.
Implement the skills and knowledge gained in your own projects.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Create and Deploy a Python Application in a Docker Container

Show steps

Solidify your understanding of Docker by building and deploying a Python application in a container, reinforcing the practical aspects covered in the course.

Browse courses on Containers

Show steps

Set up a Docker development environment.
Create a simple Python application.
Build a Docker image for your application.
Deploy the container to a cloud platform or local host.
Monitor and manage the container.

Follow a Kubernetes Tutorial Series

Show steps

Supplement your understanding of Kubernetes by following a guided tutorial series, reinforcing the concepts covered in the course.

Browse courses on Kubernetes

Show steps

Identify a reputable tutorial series on Kubernetes.
Set aside dedicated time for working through the tutorials.
Follow the instructions carefully, taking notes and experimenting with the examples.
Troubleshoot any issues encountered during the tutorials.

Build a Microservices Application Using Docker and Kubernetes

Show steps

Deepen your understanding of Docker and Kubernetes by building a real-world microservices application, leveraging the skills and concepts learned in the course.

Browse courses on Docker

Show steps

Design the architecture of your microservices application.
Create Docker containers for each microservice.
Deploy the microservices to a Kubernetes cluster.
Configure load balancing and service discovery.
Monitor and maintain the application.

Contribute to a Docker or Kubernetes Open Source Project

Show steps

Enhance your understanding by contributing to an open source project related to Docker or Kubernetes, gaining valuable real-world experience and deepening your knowledge.

Browse courses on Docker

Show steps

Identify a suitable open source project on platforms like GitHub.
Review the project's documentation and codebase.
Identify an area where you can contribute, such as bug fixes or feature additions.
Submit your code contributions and engage with the project maintainers.

Career center

Learners who complete Python in Containers will develop knowledge and skills that may be useful to these careers:

Docker Captain

Docker Captains are community leaders who are recognized for their expertise in Docker and container technologies. This course in Python in Containers will help those who wish to become Docker Captains to build a strong foundation in containerization, as well as learn how to package and deploy Python code in containers.

See salaries and explore the career path for Docker Captain

Machine Learning Engineer

Machine Learning Engineers are responsible for the development and deployment of machine learning models. This course in Python in Containers may be useful for those who wish to become Machine Learning Engineers, as they will learn how to use Docker to build and deploy machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Developer Advocate

Developer Advocates are responsible for bridging the gap between developers and the products they use. This course in Python in Containers will help those in this role to build a strong foundation in containerization, which has become an increasingly important technology for deploying and managing applications.

See salaries and explore the career path for Developer Advocate

Software Architect

Software Architects are responsible for the design and architecture of software systems. This course in Python in Containers may be useful for those who wish to become Software Architects, as they will learn how to use Docker to build and deploy software systems.

See salaries and explore the career path for Software Architect

Software Developer

Software Developers are responsible for the design, development, and maintenance of software systems. This course in Python in Containers may be useful for those who wish to become Software Developers, as they will learn how to use Docker to build and deploy software systems.

See salaries and explore the career path for Software Developer

DevOps Engineer

DevOps Engineers are responsible for bridging the gap between development and operations teams. This course in Python in Containers may be useful for those who wish to become DevOps Engineers, as they will learn how to package and deploy Python code in containers, as well as how to manage containerized applications.

See salaries and explore the career path for DevOps Engineer

Web Developer

Web Developers are responsible for the design and development of websites. This course in Python in Containers may be useful for those who wish to become Web Developers, as they will learn how to use Docker to build and deploy web applications.

See salaries and explore the career path for Web Developer

Full-Stack Developer

Full Stack Developers are responsible for both the front end and back end of a website or application. This course in Python in Containers may be useful for those who wish to become Full Stack Developers, as they will learn how to use Docker to build and deploy web applications, as well as how to manage containerized applications.

See salaries and explore the career path for Full-Stack Developer

Systems Administrator

Systems Administrators are responsible for the maintenance and operation of computer systems. This course in Python in Containers may be useful for those who wish to become Systems Administrators, as they will learn how to use Docker to manage containerized applications.

See salaries and explore the career path for Systems Administrator

Data Analyst

Data Analysts use their programming knowledge to help make sense of an organization's data. This course in Python for Containers may be useful for building the skills necessary for the role, including machine learning and data science using Jupyter Notebooks.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists use their programming knowledge to help extract valuable insights from data. This course in Python for Containers may be useful for building the skills necessary for the role, including machine learning and data science using Jupyter Notebooks.

See salaries and explore the career path for Data Scientist

Data Engineer

Data Engineers create and maintain the systems that store and process an organization's data. As the role is heavily reliant on programming, this course in Python in Containers could be useful, especially for those working with machine learning and data science.

See salaries and explore the career path for Data Engineer

Front-End Developer

Front End Developers are responsible for the design and implementation of the user interface of a website or application. This course in Python in Containers may be useful for those who wish to become Front End Developers, as they will learn how to use Docker to build and deploy web applications.

See salaries and explore the career path for Front-End Developer

Technical Writer

Technical Writers are responsible for creating documentation for software and other technical products. This course in Python in Containers may be useful for those who wish to become Technical Writers, as they will learn how to use Docker to build and deploy documentation.

See salaries and explore the career path for Technical Writer

Product Manager

Product Managers are responsible for the planning and development of a product. This course in Python in Containers may be useful for those who wish to become Product Managers, as they will learn how to use Docker to build and deploy applications.

See salaries and explore the career path for Product Manager

Python in Containers

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