2.5 Containerization

Containerization is an essential component in operations as it enables deploying and running applications in a standardized, portable, and scalable way. This is achieved by packaging an application and its dependencies into a container image, which contains all the necessary code, runtime, system tools, libraries, and settings needed to run the application, isolated from the host operating system. Containers are lightweight, portable, and can run on any platform that supports containerization, such as Docker or Kubernetes.

All of this makes them beneficial compared to deploying an application on a virtual machine or traditionally directly on a machine. Virtual machines would emulate an entire computer system and require a hypervisor to run, which introduces additional overhead. Similarly, a traditional deployment involves installing software directly onto a physical or virtual machine without the use of containers or virtualization. Not to mention the lack of portability of both.

The concept of container images is analogous to shipping containers in the physical world. Like shipping containers can be loaded with different types of cargo, a container image can be used to create different containers with various applications and configurations. Both the physical containers and container images are standardized, just like blueprints, enabling multiple operators to work with them. This allows for the deployment and management of applications in various environments and cloud platforms, making containerization a versatile solution.

Containerization offers several benefits for MLOps teams. By packaging the machine learning application and its dependencies into a container image, reproducibility is achieved, ensuring consistent results across different environments and facilitating troubleshooting. Containers are portable which enables easy movement of machine learning applications between various environments, including development, testing, and production. Scalability is also a significant advantage of containerization, as scaling up or down compute resources in an easy fashion allows to handle large-scale machine learning workloads and adjust to changing demand quickly. Additionally, containerization enables version control of machine learning applications and their dependencies, making it easier to track changes, roll back to previous versions, and maintain consistency across different environments. To effectively manage model versions, simply saving the code into a version control system is insufficient. It’s crucial to include an accurate description of the environment, which encompasses Python libraries, their versions, system dependencies, and more. Virtual machines (VMs) can provide this description, but container images have become the preferred industry standard due to their lightweight nature. Finally, containerization facilitates integration with other DevOps tools and processes, such as CI/CD pipelines, enhancing the efficiency and effectiveness of MLOps operations.