Container Strategies for Data Science

What’s it about?

In this whitepaper, we discuss the fundamentals and characteristics of containers in AI product development and define five essential layers of a container strategy for Data Science.

The triumph of containers has turned the IT world upside down in recent years. The deployment of applications of all kinds is standardized and simplified by containers. Containers have also become indispensable for data science workflows thanks to a host of arguments:

Efficiency: Containers share the OS kernel of the host machine and therefore do not need to virtualize hardware. As a result, containers are lighter than virtual machines and use infrastructure more efficiently, resulting in less infrastructure investment.

Speed: Since containers do not require their own operating system, they are started, stopped, and replaced within seconds. This reduces downtime in case of container failures, accelerates the development process and the delivery of AI products.

Portability: Containers are platform-independent. Once containerized software is ready to run, it will run on any container-friendly platform. This makes deploying models more accessible than ever. Data scientists and engineers no longer need to spend their time getting developed products to run on foreign platforms.

Scalability: Orchestration tools such as Kubernetes allow containers to scale automatically based on the current demand for the application. This manages resources more efficiently and avoids unnecessary costs. The transition to container-based architectures and workflows must be approached strategically.

The five steps on how to build the core of any container strategy for Data Science are described in detail in our whitepaper.

‍