Microservices as building blocks for AI

Author: Shane Larson
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Some ideas about how microservices could be used to compliment and be a part of an AI neural network.

It is now becoming evident that many aspects of AI that have been mainly relegated to the domain of university research are now becoming available to everyone as useful, practical tools. Deep Learning algorithms are now freely available to anyone from Google Cloud Platform, Amazon Web Services, and more. With just a bit of code and a video camera, an intermediate programmer can create an image recognition system, for example.

Another pattern in software engineering that is becoming more and more important is the pattern of the Microservice Architecture. An evolution of service oriented architecture, microservices take services to a more elemental level by breaking down monolothic applications into their distinct functional components.

The impact of this may not be immediately clear to someone just starting out working with microservices. The biggest benefit of microservices architecture is that one can build an array of applications for various uses by using a service mesh and/or a combination of API Gateways. As you increase the number of services that you maintain, you can orchestrate and leverage the microservices to create more and more applications.

The flexibility that comes from this is staggering. In the past, we were stuck with a giant monolith of an application. Now wth microservices we still have the functionality of the original application, except that we can create new applications out of the components any time by combining the microservices in different ways.

If you're interested in learning more about microservices, I created a video course titled "Building Microservices in Node.js", which is soon to be released by Packt Publishing on all of the major online outlets, such as Amazon, Safari Books, etc.

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