Before discussing containerization, it’s important to grasp the notion of micro-services. When a major application is split down into smaller services, each of those services or little operations is referred to as a micro-service, and they communicate across a network. Microservices are the polar opposite of monolithic architecture, which can be difficult to scale. If one feature malfunctions or crashes, the other features will suffer the same fate.
Another illustration is when the request for a certain feature increases significantly, we are compelled to expand resources such as hardware not only for that feature but for the entire program, resulting in unnecessary expenses. A micro-services method, which breaks down the application into a number of smaller services, can reduce this cost. Each service or feature of the application is segregated so that we may scale or upgrade it without affecting other aspects of the program. Consider breaking down the application into smaller micro-services such as intake, preparation, combination, separation, training, inference, evaluation, and postprocessing to bring machine learning into production.
Micro-service architecture is not without flaws. You’ll need the same number of virtual machines (VMs) as microservices with dependencies if you’re constructing your machine learning application on a single server. Even if the micro-service is not really running, each VM will require an operating system, libraries, and binaries, as well as extra hardware resources like CPU, memory, and disk space. This is when Docker enters the picture.
When a container isn’t operating, the resources it leaves behind become shared resources, making them available to other containers. In a container, you don’t need to include an operating system. Consider a complete solution made up of A and B. If you wish to scale out the A or add other apps, you may be constrained by the resources available if you use VMs instead of containers. If you merely scale out A and not B, then B will become a part of all container operations.
Making a deep learning model that runs on our computers is not complicated. However, it is tricky when you’re working with a customer that wants to utilize the model that can perform on various sorts of servers everywhere in the globe. Numerous factors may go wrong, including performance issues, software crashes, and an application that isn’t properly optimized.
Your ML model can easily be implemented in a single programming language but it will almost surely need to interface with other applications written in other programming languages for data preparation, data intake, and so on. Docker helps manage all of these transactions a lot easier since each micro-service is built in a distinct language. That allows for scalability as well as the quick deletion of independent services.
- Granular updates, reproducibility, ease of deployment, portability, simplicity, and lightweight are all advantages of Docker.
When a model is finished, the data scientist’s concern is that it will not repeat the findings of real-life scenarios or when the work is shared with others. And it’s not always because of the model; sometimes it’s because the stack needs to be replicated. Docker allows you to effortlessly replicate the working environment required to train and operate the ML model. Docker lets you bundle your code and resources into containers that can be moved between hosts independently of the operating system. A training model may be created on a single workstation and then quickly transferred to other clusters with greater resources, such as GPUs, more RAM, or more powerful CPUs.
Wrapping your model into an API in a container and deploying the container makes it simple to deploy and make your model available to the world. The ease with which we can develop containers using templates and have access to an open-source registry including existing user-contributed containers is also a strong argument in favor of containerizing machine learning systems. Docker enables designers to track several versions of a docker container, identify who created it with what, and revert back to previous versions.
Lastly, even if one of your ML application’s services is currently being fixed or upgraded, your ML application may still continue to function.