Python in Containers

Docker 和 Kubernetes 是当今 Python 工程师必备的技能。无论您专注于机器学习和数据科学，还是使用 Python 作为通用编程语言，您都必须了解 Docker 和 Kubernetes，因为它们构成了使用微服务架构构建的现代云原生应用程序的基础。

在本课程中，您将学习执行以下操作：

• 在 Docker 中开发和探索机器学习、数据科学和 Jupyter Notebooks

• 使用 Kubernetes 和 Docker Swarm 在生产环境中运行机器学习模型

• 将 Python 代码打包到容器中

• 在镜像注册表中发布您的容器

• 在 Docker 和 Kubernetes 中将容器部署到生产环境

• 以微服务方式构建高度模块化、基于容器的服务

• 监控和维护容器化应用

您可以通过两种方式使用课程：

• 如果您将 Python 用于机器学习和数据科学，请自上而下 – 从第 7 节开始快速培养实用的 Docker 技能，并使用第 2 至 6 节深入研究特定的容器主题

• 如果您想使用 Python 构建 Web 应用程序和微服务，请尝试自下而上的方法 – 以线性方式使用课程

MP4 | Video: h264, 1920×1080 | 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