AI-based Serverless Observability.

Software engineering is a key activity in every IT organization. It requires a lot of time and cost for managing the resources needed for the development of the product.  With a traditional architecture, it became difficult for IT operators to monitor and manage software lifecycle. The entire lifecycle can be divided into three layers, the presentation layer, application layer, and database layer. Developers need to divide focus among these layers to completely develop code that is scalable and compatible in the production environment. Hence, these cycles took ages to complete and also required a lot of software/hardware requirements. Apart from that, there is a great demand on the need for high-end resources for support. That is IT operators needed to manually set up the server, install the OS, and manage the software for high availability and scalability. Manual setup and deployment caused a lot of errors which just added up the cost of operations. In the present digital era, with digital transformation at its peak, these delays can lead to bigger losses for business and IT teams. These are unacceptable situations and need to resolve proactively. That's where the concept of serverless computing came into the picture.
Serverless Computing:
All of the above challenges were due to the approach of monolithic architecture. And the change needed here was to shift from the monolithic architecture to distributed microservice architecture. With the upcome of the digital era, most IT organizations started the use of microservice in which enabled them to achieve a distributed environment that helped in the faster development and deployment of applications. Serverless computing enabled in providing backend-as-a-service(Baas). The concept of serverless computing enabled IT operators to simply just focus on the code part without worrying about the underlying architecture. In traditional mode, they needed to focus on the presentation layer for the UI part, the application layer for the coding part which consists of multiple tools and resources and the database layer for the storage. But in the serverless computing concept, IT operators need to just keep their focus on the application layer in which they can write their code. The application layer unlike in the monolithic architecture now consists of distributed microservices that enable the propagation of code or applications. This enables the upcome of a new concept known as Function-as-a-service(FaaS). AWS Lambda enables the implementation of serverless computing and FaaS. With lambda functions the benefits are, IT operators do not need to worry about the underlying infrastructure, no need to look upon the server or software/hardware requirements. Moreover, scaling of applications became easy and operators need to only pay for what resources are used.
But with all these benefits, there possessed some challenges with serverless computing. One of the significant challenges is in observing the distributed microservices architecture and serverless application. Let's take a deeper look into the observability challenges with serverless applications.
Observability Challenges with Serverless Applications:
With high distributed microservice architecture, monitoring of applications became difficult. As in serverless application, where application code is function-as-a-service observability is quite challenging as each function need to be monitored and analyzed, and get proper alerts where in which IT operators can resolve them. Moreover, a lot of logs and metrics need to be monitored from all these functions to derive a proper root cause of an issue. But with a traditional monitoring system, this process became challenging. An advanced mechanism was needed here to resolve the issue. And that advanced mechanism is the application of AI in observability.
AI-based Observability for Serverless Applications:
AI-based observability helped in providing end-to-end visibility of the entire serverless applications. AI-enabled observability helps in collecting metrics, logs, and traces and correlates them to effectively identify any outliers of issues in the data generated from the functions. That is, a single pane of glass view is made possible for the entire application functions across the entire infrastructure. This enabled IT operators to view and operate in a single window. With the implementation of AI-based observability, IT operators were able to proactively monitor and analyze if there are issues from the data. 
As AI-based observability enables correlating metrics, logs, and traces, it enables identifying anomalies from them. That is, it performs an anomaly detection mechanism to identify if there is an unknown problem using advanced machine learning and artificial intelligence mechanism. This enables the automated identification of issues wherein which the IT operators need not worry about issues and just focus on the coding part. Some of the other significant benefits are, automated event-driven triaging in which, with observability, we can perform cross-domain event analysis. This analysis helps in capturing every error pattern, and several such run time and application infrastructure events. AI-based incident recognition can be also performed in which multiple events will be correlated together to identify the root cause of an issue. 
With the potential benefits of AI-based observability, serverless applications can be easily be observed and analyzed to get insights. This enables the ease of management of this application in a distributed microservice architecture.
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