How Serverless computing is making AI development easier

AI has been around for quite some time, enabling developers to build intelligent apps that cater to the needs of their users. Not only app developers, businesses are also using AI to gain insights from their data such as their customers’ buying behaviours, the busiest time of the year, and so on. While AI is all cool and fascinating, developing an AI-powered app is not that easy. Developers and data scientists have to invest a lot of their time in collecting and preparing the data, building and training the model, and finally deploying it in production.

Machine learning, which is a subset of AI, feels difficult because the traditional development process is complicated and slow. While creating machine learning models we need different tools for different functionalities, which means we should have knowledge of them all. This is certainly not practical. The following factors make the current situation even more difficult:

Scaling the inferencing logic

Addressing continuous development

Making it highly available

Deployment

Testing

Operation

This is where serverless computing comes into picture. Let’s dive into what exactly serverless computing is and how it can help in easing AI development.

What is serverless computing?

Serverless computing is the concept of building and running applications in which the computing resources are provided as scalable cloud services. It is a deployment model where applications, as bundle of functions, are uploaded to a cloud platform and then executed.

Serverless computing does not mean that servers are no longer required to host and run code. Of course we need servers, but server management for the applications is taken care of by the cloud provider. This also does not implies that operations engineers are no longer required.

In fact, it means that with serverless computing, consumers no longer need to spend time and resources on server provisioning, maintenance, updates, scaling, and capacity planning. Instead, all of these tasks and capabilities are handled by a serverless platform and are completely abstracted away from the developers and IT/operations teams. This allows developers to focus on writing their business logic and operations engineers to elevate their focus to more business critical tasks.

Serverless computing is the union of two ideas:

Backend as a Service (BaaS): BaaS provides developers a way to link their application with third-party backend cloud storage. It includes services such as, authentication, access to database, and messaging, which are supplied through physical or virtual servers located in the cloud.

Function as a Service (FaaS): FaaS allows users to run a specific task or function remotely and after the function is complete, the function results return back to the user. The applications run in stateless compute containers that are event-triggered and fully managed by a third party.

AWS Lambda, Google Cloud Function, Azure Functions, and IBM Cloud Functions, are some of the serverless computing providers which enable us to upload a function and the rest is taken care for us automatically.

Read also: Modern Cloud Native architectures: Microservices, Containers, and Serverless – Part 2

Why serverless is a good choice for AI development?

Along with the obvious advantage of hassle free server management, let’s see what else it has to offer for your artificial intelligence project development:

Focus on core tasks

Managing servers and deploying a machine learning model is not a good skill match for a data scientist or even for a machine learning engineer. With serverless computing, servers will conveniently vanish from your development and deployment workflow.

Auto-scalability

This is one of the key benefits of using serverless computing. As long as your model is correctly deployed on the serverless platform, you don’t have to worry about making it scale when your workload raises. Serverless computing gives all businesses, big and small, the ability to use what they need and scale without worrying about complex and time-consuming data migrations.

Never pay for idle

In traditional application deployment models, users need to pay a fixed and recurring cost for compute resources, regardless of the amount of computing work that is actually being performed by the server. In serverless computing deployment, you only have to pay for service usage. You are only charged for the number of executions and the corresponding duration.

Reduces interdependence

You can think of machine learning models as functions in serverless, which can be invoked, updated, and deleted. You can do this any time without having any side effect on the rest of the system. Different teams can work independently to develop, deploy, and scale their microservices. This greatly simplifies the orchestration of timelines by Product and Dev Managers.

Abstraction from the users

Your machine learning model will be exposed as a service to the users with the help of API Gateway. This makes it easier to decentralize your backend, isolate failure on a per-model level, and hide every implementation details from the final user.

High availability

Serverless applications have built-in availability and fault tolerance. You don't need to architect for these capabilities since the services running the application provide them by default.

Serverless computing can facilitate a simpler approach to artificial intelligence by removing the baggage of server maintenance from developers and data scientists. But nothing is perfect, right? It also comes with some drawbacks, number one being, vendor lock-in. Serverless features varies from one vendor to another, which makes it difficult to switch vendors. Another disadvantage is decreased transparency. Your infrastructure is managed by someone else, so understanding the entire system becomes a little bit difficult.

Serverless is not an answer to every problem but it is definitely improving each day making AI development easier.

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