Why data scientists should love Linux containers

Containers are a hot technology for application developers, but they also provide key benefits for data scientists. William Benton details the advantages of containers for data scientists and AI developers, focusing on high-level tools that will enable you to become more productive and collaborate more effectively.

Linux containers make it easy for teams to deploy, manage, and scale distributed applications and for operators to exploit compute capacity in the cloud. Although it might not be obvious, a great foundation for production applications can also support the exploratory work of data scientists and machine learning engineers. William Benton details the advantages of containers for data scientists and AI developers, focusing on high-level tools that will enable you to become more productive and collaborate more effectively. To provide context, William briefly explains what containers are and why developers love them. He then covers several key benefits of containers for data scientists, focusing on repeatability, collaboration, scalability, and compliance. You’ll learn how containers fulfill the promise of reproducible research, ease moving techniques from prototype to production, enable painless publishing and collaboration workflows, and empower you to safely develop techniques against sensitive data in a production environment from the comfort of your laptop. There are myriad tutorial resources explaining how to build and run container images, but these largely assume an audience whose primary responsibilities include packaging, releasing, and managing applications. William focuses on why data scientists should care about containers and the high-level tools built on top of containers that will enhance their daily work. Data scientists will leave with a better understanding of the advantages of containers and concrete suggestions for how to use higher-level tools to make their work more productive. Application and AI developers will learn about the commonalities between engineering workflows and data science workflows and leave with a better understanding of how containers can support their data scientist colleagues and enable cross-functional collaboration.