Lately, several advanced technological approaches like microservices have gained great traction in the circle of IT and software development. However, the concept of microservices is not that advanced. It has been there for more than a decade. It’s just that around 2014 the market witnessed a sudden spike in the interest among development companies and businesses alike for microservices. So, now the question is – what happened around nine years ago that changed everything for this architectural software development approach? Well, one of the significant reasons is the integration of digital technologies like AI with microservices that resulted in better outcomes. The domain of artificial intelligence has progressed lately, transforming the way microservices-based software applications are analyzed and developed. Due to this, businesses have started installing AI models as microservices to remove or add specific services without disrupting the functioning of other services. But on a larger scale, do you think deploying AI models as microservices could be a productive step? Well, this blog will encircle a few prominent reasons why you should consider it. So, let’s get started.
What Exactly Are Microservices?
When you develop an application, there are majorly three components to focus on – a client-side interface, a server-side portal, and a database. You are most probably aware of the functions of a database in software. Now, the client-side primarily deals with providing the user interface where the users interact with the application. The server-side portal is responsible for the tasks associated with managing and processing requests from the server and offering timely and accurate responses. Well, to create web-based server-side apps, one might need to pick and use an app architecture. Microservices-based architecture is one of the most common and widely used options among developers. This architectural approach is prevalent and gaining popularity for its capabilities to overcome the challenges faced by traditional monolith architecture. These challenges majorly emerge due to an increase in the app’s complexity and size.
What Made Microservices Approach a Preferred Option Over Monolithic Architecture?
Initially, it was merely an idea to deploy large and complex monolithic applications as small independent services that developers can manage and update separately. However, with time, this practice was preferred over the traditional monolithic architectural approach for its features that allow quick and easy deployment of complex applications. Besides this, it also provides developers with great options to manage and operate applications and their functionalities. Consequently, it increases the scope of scalability and flexibility in those apps. Due to these reasons, the demand for a professional artificial intelligence development company significantly increased. Businesses often realized that by deploying AI models as microservices, they can easily leverage the potential of this architectural approach.
Prominent Reasons Why Businesses Must Deploy AI Models As Microservices
Once you develop an AI model, there are a plethora of benefits you can avail of by installing it as microservices in a container. Here are some of the notable reasons why deploying AI as microservices could be profitable for businesses:
Easy to understand and work with
Due to their smaller size and simple structures, microservices are easier to comprehend compared to monolithic app architecture. Also, since they are primarily focusing on crucial business functions, you no longer need to worry about disrupting all of them while deploying a single one of them.
Better scalability and efficiency
Since each service of the app is installed independently, there is a wide scope for independent scaling for each one of them. There is no need to scale the entire application at once, ultimately saving the time and effort of developers. This helps in making the best use of computation capabilities and attaining the right balance of computing resources. All these processes further improve the efficiency of scaling.
Enhanced data accessibility
When you utilize an AI model as a service, it could get easily exposed to both the internal and external parts of the application without any need to move the code. Also, containers consist of in-built mechanisms that offer distributed data access. It helps users to take advantage of common data-centric interfaces supporting a plethora of data models.
Multiple tools and languages to choose from
By using API endpoints to deploy machine learning and artificial intelligence models as microservices, AI developers get opportunities to create models in whatever framework they want. It means they can choose either Keras, Tensorflow, or PyTorch to write models – without worrying whether the tech stack would be compatible or not. Also, it offers the feasibility to the development team to choose whatever tool or language they prefer without impacting the overall process.
Continuous delivery of complex AI and ML applications
Another area where the deployment of AI models as microservices could be beneficial for businesses is consistent project delivery. Microservices provide developers with a great possibility to install new versions of an app independently and in parallel to other services. In this way, they could make changes independently in different services, accelerating the overall deployment process. Moreover, with production-ready data automation ML platforms like Tensorflow Serving, one could easily manage different versions of a complex AI model.
Wrapping Up
The digital transformation in technology has led to several advancements in the DevOps sector. Today, there are multiple ways through which large and complex AI models can be deployed as microservices. Tech giants like Amazon and Netflix have already implemented microservices architecture and the initiative is taking their digital business by storm. These organizations have not only included microservices in the mainstream but also encouraged the tech community to leverage its benefits. All these real-life examples and the information included in this blog provide you with the best reasons to use and deploy your AI models as microservices. Now, it’s up to you how you consider this change for your business growth.