In data science, the use of Machine Learning (ML) and Artificial Intelligence (AI) tools continues to grow. The application of these tools varies greatly – from chatbots, sales forecasting, fraud prevention, all the way up to driverless cars. The possibilities of these tools seem infinite and everything we have seen so far appears to indicate that AI and ML will have a bright and prosperous future within the field of data science.

Similar to the impact that AI and ML have had on the wider IT world, a solution called Docker is making a huge impact to the data science world. Docker is a solution that wraps the components of application processes in a container, much like virtual machines, only it is more portable, easier to use and less dependent on the host operating system. Docker is quickly revolutionising the field of data science thanks to its ability to compress all the software necessary for it to run within just an image.

Docker guarantees isolated containers that have within them all the packages and tools a data scientist needs to perform their own ML and AI experiments – eliminating the time required to install these tools and learn their methods. Essentially, Docker is a lightweight virtual machine that is built from a script, and every image has a tag to mark its version. This enables data scientists to check the version of their scientific data environment. Developers can use Docker when collaborating on code with colleagues to build agile software delivery pipelines to ship new features much faster.

So, why is Docker so great for data scientists? To start, think about how many times you have heard things such as:

  • “I’m not sure why it doesn’t work on your computer, it’s working on mine.”
  • “It’s difficult to install everything from scratch for Linux, Windows and MacOS and try to create the same environment for each operating system.”
  • “I can’t install the package you used. Can you help me?”

For the most part, these concerns can be easily solved by Docker. Firstly because, if two separate environments are built from the same Docker image, everything that works on one computer will work on any other. Secondly, there’s no need to install anything manually as the components of each environment all come packed into the image. The only exception at the moment is the GPU support for Docker images, which only work on Linux machines.

In addition to the code version control, Docker helps facilitate the delivery of products and solutions to customers. Docker containerisation makes it easier to package and distribute any programming applications. Just like a container on a ship that travels around the word, data scientists can wrap all the components of an application into a Docker image and move it anywhere! With just a couple of clicks (literally!) we can install a full isolated self-contained application into a customer’s production environment, both in the cloud and on-premise.

In terms of security, with the same ease and rapidity in which it was installed, an application can be eliminated while maintaining the confidentiality of the data. Docker allows for applications running on containers to be completely separated and isolated from each other, ensuring absolute control over your data management and traffic.

Just as the entire IT world has been transformed by AI and ML, Docker is rapidly revolutionising the field of data science as we currently know it. Discover more about Incremental’s Data and AI offerings and the work we do in the field of data science.