How Serverless Architecture can aid Artificial Intelligence and Machine Learning Industries
Serverless, the new big word in the software architecture world has drawn a lot of attention from both rookies and pros in the field. Google, Amazon, and Microsoft are heavily invested in serverless computing. Serverless is not just about the hype, it promises the possibility of an ideal business implementation which sounds awesome and more importantly lighter on the budget.
What is serverless architecture?
Serverless architectures (also known as serverless computing or function as a service, FaaS) is a cloud computing execution model that incorporates third-party “Backend as a Service (BaaS) services available by any cloud services providers like AWS, Google, etc.
The cloud providers dynamically manage the allocation and provisioning of servers. It includes codes that are run in managed, ephemeral containers on a Function as a Service (FaaS) platform. By using these concepts, a serverless architecture removes much of the needed always-on server components.
Serverless architectures may benefit from significantly reduced operational cost, complexity, and engineering lead time, at a cost of increased reliance on vendor dependencies and comparatively immature supporting services.
Why go serverless?
Serverless applications are mostly managed by third-party server allocators and provisioning of servers is done dynamically based on the requirement. Its main motive is to focus on the application rather than the infrastructure. It sounds very relieving as a lot of working hours on a project go into implementing, maintaining, debugging and monitoring the infrastructure. With all that now out of the way, the developers can focus on the business goals their application serves.
Serverless architecture is breaking ground in the current business world. It is already accepted and used in production by companies like Netflix, Reuters, AOL, and Telenor. With passing time industry-wide adaptation is constantly increasing.
Serverless architecture for Artificial Intelligence and Machine Learning
Machine Learning and Deep Learning are becoming more and more essential for businesses in internal and external use. Artificial Intelligence and ML are steadily making their way into enterprise applications in areas such as customer support, fraud detection, and business intelligence.
Cloud Service Providers have contributed enormously to this growing trend. Serverless architecture changes the rules of the game — instead of thinking about cluster management, scalability, and query processing, you can focus completely on training the model.
With trending technologies like Kubernetes and Docker, one can easily switch from traditional on-premise application to a more scalable and efficient microservices-based application.
Benefits of having Serverless Architecture
Pricing
One of the major advantages of using serverless architecture to train the ML models is the pricing structure. In the traditional approach, the server was kept on even when the ML model is not utilized. But when serverless architecture is used it becomes easy to reduce the cost as the cost model of Serverless is execution-based. You are charged for the number of executions or number of API calls you make to use the ML model on the cloud.
Setting up work environments
Setting up different environments for serverless is as easy as setting up a single environment. Given its pay-per-use, it is a large improvement over the traditional servers. We no longer require to set up dev, staging and production machines.
Scalability
When you push your ML models or AI applications as serverless applications or microservices it becomes very easy to scale up according to requirement. Only a single line of code or select buttons provided by providers can initiate scaling of the application.
Function as a Service (FaaS)
The key-properties of FaaS are Independent, server-side automation and logical functions. It completely manages the servers and facilitates event-driven and instantaneous scaling.
Principles of FaaS:
- Complete abstraction of servers away from the developer
- Billing based on consumption and executions, not server instance sizes
- Services that are event-driven and instantaneously scalable
Ephemeral
FaaS is designed to start up quickly, do the job and shut down. They do not remain active when not required by the application. This helps in saving the execution time. As long as the task is performed the underlying containers are scrapped.
Conclusion
When you want to deploy an ML model on the server it has a lot of drawbacks and complexity as the servers need to be patched, attended to regularly, modified and need hardware maintenance too. Serverless architecture saves you from all this pain so that you can work on the functionality of your application and its uses without worrying about the server maintenance.
Serverless Architecture is a very exciting concept, but when it comes to reality, it cannot be ignored that it too has limitations.
As the validity and success of any architecture depend on the business requirement, similarly the success of any ML model on a serverless architecture can be guaranteed if it is used In proper place.
