MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch

March 24, 2020 | ISBN: 9781800200753 | English

Duration: 114 Lessons (23h 48m) | Size: 6 GB

All about containers, Docker, and Kubernetes for Python engineers.

Learn

Build container images having Python applications and ship them to Docker Hub

Run Jupyter Notebooks and create virtual machines in Docker

Use Docker Desktop for Windows Pro and macOS and Docker Toolbox for Windows Home

Create custom container images from scratch and automate container image builds with Dockerfile

Design Flask and Django multi-container deployments and automate them with Docker Compose

Containerize TensorFlow models into microservices and use Kubernetes with Minikube on a development host

Deploy complex multi-container applications in Docker Swarm and Kubernetes

About

Docker and Kubernetes are must-have skills for Python engineers these days. Whether your focus is on machine learning and data science or you use Python as a general programming language, you must understand Docker and Kubernetes, as they form the basis of modern cloud-native applications built using microservice architectures.

In this course, you’ll learn to do the following:

• Develop and explore machine learning, data science, and 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 to production, both in Docker and Kubernetes

• Build highly modular, container-based services in a microservices way

• Monitor and maintain containerized apps

You can use the course in two ways:

• If you use Python for machine learning and data science, go top-down - start with section 7 to quickly develop practical Docker skills and use sections 2 to 6 to delve deeper into specific container topics

• If you want to use Python for building web apps and microservices, try the bottom-up approach - use the course in a linear way

Features

Become well-versed with using Docker tools to create top-class containers running your Python code

Master Docker runtime tools such as Compose and Swarm

Design your applications to run on Kubernetes and master writing Kubernetes object declarations