Python in Containers

Python in Containers, available at $84.99, has an average rating of 4.58, with 115 lectures, based on 470 reviews, and has 4362 subscribers.

You will learn about 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 This course is ideal for individuals who are Python Programmers or Data Scientists or Machine Learning Engineers It is particularly useful for Python Programmers or Data Scientists or Machine Learning Engineers.

Enroll now: Python in Containers

Summary

Title: Python in Containers

Price: $84.99

Average Rating: 4.58

Number of Lectures: 115

Number of Published Lectures: 115

Number of Curriculum Items: 115

Number of Published Curriculum Objects: 115

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Python Programmers
Data Scientists
Machine Learning Engineers

Target Audiences

Python Programmers
Data Scientists
Machine Learning Engineers

Docker and Kubernetes are the Must-Have Skillsfor 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” Abbi1680@gmail.com
“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 butskills 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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction & Agenda Overview

Lecture 2: The Concept of Linux Containers

Lecture 3: Containers Explained

Lecture 4: Build, Ship, Run

Lecture 5: Introduction to Docker

Lecture 6: Ecosystem of Container Technologies

Lecture 7: Introduction to Container Orchestration

Lecture 8: Python App in Docker Container

Lecture 9: Shipping the Image to Docker Hub

Lecture 10: Running our App in Various Environments

Chapter 2: Docker Deep Dive

Lecture 1: Installing Docker for a Developer

Lecture 2: Create Docker ID

Lecture 3: Play with Docker

Lecture 4: Install Docker on Ubuntu

Lecture 5: Install Docker on CentOS

Lecture 6: Docker on Linux – Security Warning

Lecture 7: Docker Desktop on Windows Pro

Lecture 8: Introduction to Windows Containers

Lecture 9: Docker Desktop on MacOS

Lecture 10: Docker Toolbox for Windows Home

Lecture 11: Docker Desktop on Windows 10 Home with WSL2

Lecture 12: Running Containers with Docker

Lecture 13: Integrating Containers with a Host System

Lecture 14: Container Images

Lecture 15: Managing Containers

Lecture 16: Running Multiple Containers

Lecture 17: Container Networking

Lecture 18: Data Persistency – Volumes

Lecture 19: Dockerfile Introduction

Lecture 20: Docker Hub Introduction

Lecture 21: Python Base Images

Lecture 22: Docker GUIs Part 1 – Kitematic

Lecture 23: Docker GUIs Part 2 – Portainer

Lecture 24: Docker Machine Overview

Lecture 25: Docker Machine with VirtualBox

Lecture 26: Docker Machine with Hyper-V

Lecture 27: Docker Machine on AWS Cloud Hosts

Lecture 28: Docker Machine on Google Cloud Hosts

Chapter 3: Build Container Images

Lecture 1: Elements of Containerized Python Project

Lecture 2: Lifecycle of Containerized Python Project

Lecture 3: Design Principles for Containerized Python Apps

Lecture 4: Manual Image Build Process

Lecture 5: Dockerfile – Automation of Image Build

Lecture 6: Dockerfile Commands – Introduction and FROM

Lecture 7: Dockerfile Commands – WORKDIR, COPY, ADD

Lecture 8: Dockerfile Commands – RUN

Lecture 9: Dockerfile Commands – ENV, LABEL, USER

Lecture 10: Dockerfile Commands – VOLUME and EXPOSE

Lecture 11: Dockerfile Commands – ENTRYPOINT and CMD

Lecture 12: Parametrizing Dockerfiles with ARG

Lecture 13: Building and Running Reusable Images

Lecture 14: Build time versus Run time Execution

Lecture 15: Building smaller Images

Lecture 16: Multistage Image Build

Lecture 17: Building Custom Python Images

Lecture 18: Build Base Images from Scratch

Lecture 19: Dockerizing PyTest and Pdb – Simple Case

Lecture 20: Django Containerization for Development

Lecture 21: Django Containerization for Production

Lecture 22: Application Servers to Run Django and Flask

Lecture 23: Production–grade Database Engine – PostgreSQL

Lecture 24: Production–grade Database Engine – MariaDB

Lecture 25: Implementing Proxy Server

Lecture 26: The need of Automation

Chapter 4: Ship Containers

Lecture 1: Shipping Images

Lecture 2: Image Registries and Repositories

Lecture 3: Review of Key Cloud Registries

Lecture 4: Review of Local Registry Technologies

Lecture 5: GitHub and Docker Hub Integration

Lecture 6: GitLab Container Image Build Workflow

Lecture 7: Vulnerability Scanning of Images

Chapter 5: Run Containers in Docker

Lecture 1: Running Production Containers in Docker

Lecture 2: Docker Compose – Introduction

Lecture 3: Docker Compose File – Version and Volumes

Lecture 4: Docker Compose File – Networks

Lecture 5: Docker Compose File – Services

Lecture 6: Managing Images with Docker Compose

Lecture 7: Application Lifecycle with Docker Compose – Part 1

Lecture 8: Application Lifecycle with Docker Compose – Part 2

Lecture 9: Introduction to Docker Swarm

Lecture 10: Provisioning Swarm with Docker Machine

Lecture 11: Standalone Containers in Swarm

Lecture 12: Services in Swarm

Lecture 13: Service Modes and Ingress Routing Mesh

Lecture 14: Application Stack in Swarm – Part 1

Lecture 15: Application Stack in Swarm – Part 2

Lecture 16: Application Environment in Swarm – Part 1

Lecture 17: Application Environment in Swarm – Part 2

Lecture 18: Application Lifecycle in Swarm

Lecture 19: Summary of Docker Runtime Environment

Chapter 6: Run Containers in Kubernetes

Lecture 1: Introduction to Kubernetes

Lecture 2: Helicopter View of Kubernetes as the Application Platform

Lecture 3: Installing a Small Kubernetes Cluster

Lecture 4: Running Simple Application in Minikube

Instructors

Kris Celmer
Seasoned IT Infrastructure Professional – Cloud Expert

Rating Distribution

1 stars: 8 votes
2 stars: 10 votes
3 stars: 26 votes
4 stars: 127 votes
5 stars: 299 votes

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