Learn Docker "infrastructure as code" technology to define a system for performing standard but non-trivial data tasks on medium- to large-scale data sets, using Jupyter as the master controller.
It is not uncommon for a real-world data set to fail to be easily managed. The set may not fit well into access memory or may require prohibitively long processing. These are significant challenges to skilled software engineers and they can render the standard Jupyter system unusable. 

As a solution to this problem, Docker for Data Science proposes using Docker. You will learn how to use existing pre-compiled public images created by the major open-source technologies―Python, Jupyter, Postgres―as well as using the Dockerfile to extend these images to suit your specific purposes. The Docker-Compose technology is examined and you will learn how it can be used to build a linked system with Python churning data behind the scenes and Jupyter managing these background tasks. Best practices in using existing images are explored as well as developing your own images to deploy state-of-the-art machine learning and optimization algorithms.
What  You'll Learn 

• Master interactive development using the Jupyter platform
  
• Run and build Docker containers from scratch and from publicly available open-source images
  
• Write infrastructure as code using the docker-compose tool and its docker-compose.yml file type
  
• Deploy a multi-service data science application across a cloud-based system
  

Who This Book Is For

Data scientists, machine learning engineers, artificial intelligence researchers, Kagglers, and software developer

学习Docker "基础设施即代码 "技术，定义一个在中到大规模数据集上执行标准但非琐碎的数据任务的系统，使用Jupyter作为主控制器。

现实世界中的数据集不能被轻易管理的情况并不少见。该数据集可能不能很好地适应访问内存，或者需要过长的处理时间。这些对熟练的软件工程师来说是重大的挑战，它们会使标准的Jupyter系统无法使用。

作为这个问题的解决方案，Docker for Data Science建议使用Docker。你将学习如何使用由主要开源技术--Python、Jupyter、Postgres--创建的现有预编译公共镜像，以及使用Dockerfile来扩展这些镜像以适应你的特定目的。我们研究了Docker-Compose技术，你将学习如何使用它来建立一个链接系统，让Python在幕后处理数据，让Jupyter管理这些后台任务。探讨了使用现有镜像的最佳实践，以及开发自己的镜像来部署最先进的机器学习和优化算法。

你会学到什么 

掌握使用Jupyter平台的交互式开发

从头开始运行和构建Docker容器，并从公开可用的开源镜像中获取。

使用docker-compose工具及其docker-compose.yml文件类型将基础设施作为代码来编写

在基于云的系统中部署一个多服务的数据科学应用

本书适用对象

数据科学家、机器学习工程师、人工智能研究员、Kaggler和软件开发人员