By deploying AI models as microservices, businesses can seamlessly add and remove services from an application without disrupting its other services.
Most applications that are developed have two major components, a database, and a server-side application. The server-side application carries out the tasks of processing server requests and providing an accurate response. These web-based server-side applications can be created using different architectures, one of which is the microservice architecture. Microservices are the architectural technique to overcome the growing challenges of the traditional monolith architecture that has grown due to an increase in the size and complexity of applications. It is an idea of deploying large monolithic applications as small independent services that can be managed and updated individually. It is the preferred option while choosing between monolithic and microservices for deploying software applications as it enables quick and reliable deployment of large and complex applications. Also, it provides simplicity in managing and operating applications and increases its scalability and flexibility. Thus, by establishing AI models as microservices, enterprises can leverage all the benefits of microservices architecture.
Advantages of deploying AI models as microservices
Once an AI model is developed, there are various significant advantages of deploying it in a container as microservices.
Creating an AI model that works only on the developer’s computer is simple, but creating a model that can scale and function in all the computers across the globe becomes challenging. And this is where microservices architecture can be very beneficial. Also, standard non-AI software applications are usually written in one single programming language like Java or C++. But, AI applications are mostly created in a way that uses various programming and data science languages to operate. For instance, many AI applications start by using one data science language and end by using another for various purposes like data ingestions, modeling, and predicting. And since each service in a microservice architecture can be written in different languages, it becomes easy to make diverse programming and data science languages to work together, which further increases the scalability of AI models. Also, the microservice architecture allows independent adding or removing a service from a container that enhances the scalability of the entire container.
With a microservices architecture, organizations do not need to update the entire AI model to update a single service. For instance, a chatbot performs various services to provide different benefits to businesses. But, suppose a business wants to update the welcome message, then there will be no need to update the entire application embedded in a chatbot. Organizations can update only the welcome message service without interfering with other services.
Improving fault isolation
If a business is deploying an AI model as a single big application and not as microservices, then the failure of a single service will fail the entire model. But, if a business is using microservices architecture to deploy AI models, then the failure of a single service will not drastically affect other services of the model.
With the advancement in AI technology, there are many initiatives taken by individuals and organizations that help to deploy AI models as microservices. These individuals and organizations take care of managing all the microservices in a container efficiently. For instance, it is crucial to start the functioning of the right containers at the right time and make them communicate with each other so that developers can manage and handle memory considerations.