Cloud computing has changed the software modeling and solutions pattern. With more demand and customization in place, it’s now time for more economical yet efficient and intelligent solutions. In this context, serverless computing is redefining cloud computing, whereas machine learning (ML) provides next-generation built-in intelligent software. Furthermore, serverless computing gives AI application development efficiency, simplicity, and productivity.
Cloud-based PaaS leverages AI at a rapid pace as well as effectively. This also drives growth, investment, and interest in AI technology. Simultaneously, cloud PaaS providers serve a broader range of businesses and demand for AI-based systems. Not to mention, PaaS is essentially the biggest driving force behind the AI revolution.
Interestingly, as more and more companies are starting investing in PaaS for streamlined operations, PaaS providers are investing more in serverless computing, allowing more rapid testing in the AI space. Hence, the rapid development of PaaS capabilities is no doubt pushing AI technology forward. This is especially valid for startups and small teams with relatively less access to complex and reliable AI models. This also helps in the rapid prototyping of new AI services.
What is Serverless computing?
Serverless computing is a cloud execution model. A cloud provider dynamically allocates compute resources and storage for a particular piece of code and then charges the user for it. Hence, it is not like there is no server included in the whole process, but their provisioning and maintenance burden is not on the user and is entirely taken care of by the provider.
Furthermore, this is an event-driven design and development paradigm, which means the code is invoked when a request is triggered. Also, users only pay for the services they are using instead of paying a flat monthly fee. No cost is associated here with downtime or idle time. Here server is not eliminated, but resource considerations are moved into the background as per the design process.
Thus, with the Serverless computing concept, the CAPEX and OPEX cost decreases significantly due to fewer deployment times and fewer resource involvement. This enables developers to build complex systems quickly as different services are combined and composed in a loose orchestration. AWS Lambda is an example of a public cloud Serverless computing.
So, in Serverless computing, the user uploads a function, specifies the required resources, and uploads the Cloud function. Then the Cloud vendor (Amazon, Microsoft, Google) does the server’s provisioning and deploys its function. So, the user doesn’t need to think about the server, or everything happens in a Serverless way. The provisioning and decommissioning of servers are performed by the cloud vendors based on the demand for functions that goes up and down. All of this is transparent to the end-user.
The serverless architecture consists of two different concepts –
1.Function as a Service (FaaS)
FaaS works on an event-driven basis. Also, it performs as a long-running asynchronous task via Serverless architectures. In this case, server-side logic is written by the application developer. However, unlike traditional architectures, it’s run in stateless compute containers that are event-triggered and fully managed by a third party. AWS Lambda is one of the examples of FaaS.
Furthermore, FaaS offerings do not depend on a specific framework or library for its coding. Instead, FaaS functions are regular applications regarding the language and environment.
2. Backend as a Service (BaaS)
In the case of Backend as a Service, the client application directly interacts with the data layer through an authentication layer. Here no custom logic is written on the server-side. Instead, it is solely on the client.
What are the advantages of Serverless computing in AI development?
The most important benefit of using serverless computing in AI is hassle-free server management. The service consumers do not hold any responsibility to maintain or administer the server, and it’s all the service vendor’s responsibility. Furthermore, serverless computing offers built-in services provided by Cloud Service Provides, which are fully managed, supports elasticity, fault-tolerant, and offer enterprise-level integrated security along with automatic scaling. Hence, it is more productive concerning developers’ activity.
Apart from that, there are the following benefits of using serverless computing in AI development –
Auto-scalability
The serverless architecture leverages flexibility towards scalability and growth. Whether it is a small or big business, the cloud allows users to use what they need. So, they can scale up without worrying about complex and time-consuming data migrations. Furthermore, if the application is deployed correctly, there is no issue with scaling it, even if the workload increases.
Focus on core tasks
With serverless computing, there is no headache of managing servers. Hence, data scientists or machine learning engineers can only focus on AI development and deploying AI models.
Pay only for the functions you use
In traditional application deployment models, you need to pay a recurring and fixed cost for computing resources. This could be more than the amount of computing work is actually being performed by the server. However, in serverless computing deployment, the user needs to pay only for the service. It is only for the number of executions and their duration during development and deployment.
Less interdependency
Machine learning models are treated as serverless functions that you can invoke, update, and delete as required. This can be done without interrupting the rest of the system. Furthermore, different teams can work independently to develop, deploy, and scale their individual AI models.
Abstraction from the users
The machine learning model is exposed as a service through API Gateway to the users in the serverless computing model. Hence, you can easily decentralize the backend as serverless creates an abstraction to the users. Simultaneously, this architecture isolates failure on a per-model level while hiding every implementation detail from the final user.
High availability and fault tolerance
Serverless applications have built-in features of availability and fault tolerance by default. Hence, while modeling AI applications, there is no need to architect for these capabilities. Furthermore, Serverless computing facilitates a simpler approach to artificial intelligence as it removes the overload of server maintenance from developers and data scientists.
Challenges associated with Serverless computing
1.Security concerns: Of course, it’s a big relief to delegate the backend services to the cloud vendors in a Serverless model. However, it automatically brings another concern for application data, that is – security concern. Since the personal data are stored in vendor assigned servers and executing code can be shared in multi-tenancy mode, there is always a risk associated with a data leak. For example, broken authentication is a common issue in serverless computing, where some of the functions may expose public web APIs. In contrast, some consume events from different sources leading to unauthorized authentication.
2.Testing and debugging is a real challenge: When there is no control on backend processes, furthermore application is divided into different functions, it becomes a real challenge for developers to test or debug the application.
3.There is a concern with the pay-as-you-go model: No doubt it is a cost-benefit solution in a Serverless computing model, but what if you need to run the code for a long time? Sometimes it may cost even more than traditional cloud infrastructure!
4.Vendor lock-in: Depending on a single vendor for all backend services is an advantage concerning its reliability. However, switching from one vendor to others may cost a lot as each vendor have their own set of workflows.
5. EDA-based programming: Serverless computing follows event-driven architecture (EDA) where applications are designed to collect functions and wired by the architecture (EDA). However, while designing serverless solutions, if the use case becomes complex, then we have to think differently. EDA-based programming is harder to debug. So, the final architecture is more complex with a harder logical flow.
6. Complex: Serverless is agile as long as the programmers follow its model but become complex if the programmer resists the model. This is a lack of flexibility, which may limit serverless adoption in particular use cases.
7. Latency challenges: Handling state using the stateless function is a concern with serverless. As per the best practices on the current architecture, the state can be stored in platform services such as shared file systems, databases, or messaging systems. Additionally, serverless belongs to two kinds of latency challenges: high tail latencies and cold-starts.
8. Lack of standards: There is a lack of concerns and standards about vendor lock-in, which poses a significant risk to serverless adoption. Here the real concern is the platform services necessary by those functions and not serverless functions. It is hard to abstract away those services effectively.
9. Conflict with DevOps – In the Serverless computing model, the software developer is free from responsibility for understanding his code’s system requirements. However, this is one of DevOps’ important aspects, where mutual understanding regarding each other’s needs is essential between the developers and operators.
Final verdict
Serverless can’t answer every problem. However, it is definitely improving each day and making AI development easier. And this will continue to drive AI innovation, becoming the biggest driving force behind AI projects in the future. Not to mention, companies that seize the opportunities serverless and AI are going to be miles ahead in the competition over the next decade.