The world of platform building has seen thousands of solution platforms being built for general as well as specific purposes. Platforms are built to aid solution building with the help of its components that perform certain industry-specific operations/tasks. Such platforms always need careful and rapid design changes or additional components building. This need is due to the increase of functionalities they witness when they race ahead to provide services to different domains. The consequence of the absence of a smarter approach to platform building has always been visible in the form of bulky platforms, with loads of unnecessary services, development of duplicate services with minute differences to cater to different domains, heavy portfolios to choose the right services from, etc.

Artificial Intelligence (AI) has come a long way and become a part of every development to help build concise and smarter solutions. Today the areas of AI, such as machine learning (ML), deep learning (DL), and General Intelligence, have penetrated deep into solution building. They are helping build concise components capable of being small yet smart enough to cater to the needs of various domains through continuous learning and unaided solution building.

The AI-powered solutions, in combination with microservices-based solution designing and building approach, can help build platforms capable of doing far more than what earlier platforms could do. The platforms thus built with such an approach will be smart enough to provide ‘on the spot’ assistance to clients for their needs such as data storage, network bandwidth identification for optimum performance, analytical service throughout solution building and deployment, deployment strategies and assistance, etc.

Analysis and Idea Development

Earlier solutions were based on the monolithic architecture style. Applications developed in this fashion had a modular architecture at a logical level. However, using monolithic architecture for application development did have some advantages like ease of development, testing, and deployment, so it was a preferred approach. Unfortunately, this simple approach had a lot of limitations- there was a need to develop a service for every requirement, and thus the entire application became bulky and complex to maintain and upgrade. Also, the services used in one solution were not reusable for other solutions. Successful applications grew over time and then eventually became huge and difficult to maintain moving forward.

Organizations around the world have understood that they need more than just a solution to meet the ever-changing and ever-expanding requirements of their clients across the domains while staying relevant to the market. Microservices architecture-based platform can help in such dynamic situations.

Microservices architecture is gaining a lot of popularity these days among IT organizations. It has significant benefits when delivering complex enterprise applications in iterative models like agile development. Using this architecture, we can develop services that are fine-grained, reusable, stateless, event-driven, loosely coupled, on-demand, easily discoverable, and independently scalable and manageable. When compared with the monolithic architecture, microservices-based architecture services can be created, tested, and deployed more quickly and independently. There is a flexibility of choosing the language or technology to develop such services.

Moreover, developing AI platforms having a flavor of microservices architecture is much better than just plain microservices. Such platforms can look at a requirement, identify and combine the existing generic microservices to create a specialized microservice for the requirement.

Microservices based modular approach for AI platform

Having modularized service accelerators gives an advantage of reusability in other areas of solution but having service accelerators as independent service provides the huge advantage of reusability across different solutions as and when required.  Implementing AI on top of them will help in deployment of different microservices based on the requirement. Deployment microservices are a special type of microservices that are AI integrable and basis the inputs, estimates, and optimizes the number of resources to be used for a requirement, thereby reducing the cost. The deployment microservices also automate the entire process of deployment of the solution.

AI Platform Architecture powered Microservices’ Modular Design and Rapid Deployment ability

This platform not only integrates AI engines with microservices and makes them smart, but it has AI engines that use generic microservices and makes domain-specific microservices. The AI engines, based on the requirements of the customers, will suggest to them microservices, databases,  bandwidth, and how the deployment should be done. The AI’s in this platform will also take care of the microservices which are developed to build the platform and manage their entire maintenance, communications, lifecycle, etc. Microservices architecture supports containerization and orchestration, thereby helping the AI to create any number of instances of different microservices that are required for a solution. If any microservice is not available, then the AI will create a service by using multiple fundamental microservices available and use them all together as a single unit in a solution for a requirement. For example, If we require a ticket counting or product counting microservice, that is not available, then the AI engine can use the generic counting microservice and wrap it to create a ticket or product counting microservice. So, in short, the AI engine will continuously learn what different microservices it can build for different domains and in what way. There are many generic fundamental microservices available on the platform, which the AI engine will combine and build a new solution.

The future platform that we envision will be based on various generic level engines and finely tuned functionality-based microservices. These, in combination, will work towards providing solutions to various domain-based requirements on their own, without the need of developing new services.

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Piyush Shewale and Nilesh Parekh

Posted by Piyush Shewale and Nilesh Parekh

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