Optimizing AI Workflows with Infrastructure-as-Code and Serverless Cloud Patterns

Phanish Lakkarasu

Abstract: Today, many applications exploit artificial intelligence and machine learning algorithms to perform various tasks. However, creating a solution for tasks like image prediction or language translation generally takes significant effort by many teams. These effort-intensive actions can potentially be shortened by creating fully serverless cloud pipelines that anyone can customize and employ. The problem is that despite the numerous cloud services that support multiple AI ML frameworks, creating and fully cloud-dependent execution will take considerable time. Many sectors are migrating to applying artificial intelligence and ML paradigms on their day-to-day tasks. However, the internal logic and learning process need to be regulated and understood. Recent research shows that interest in cloud usage is proliferating daily. Major cloud services may attract many customers if they make their services cost-effective and pleasant to use. Tech-savvy people create off-the-shelf clever cloud services to perform daily tasks like object detection, classification, recognition, classification, and image captioning without in-depth knowledge about the composition underlying knowledge. Despite web applications that hold backend execution logic, training and executing this knowledge are costly and limited to leading technology companies. Such reasons inspire using emerging technologies to create modular and fully serverless cloud capabilities. With the recent advent of serverless cloud computing architecture, with low costs, management overheads, and serverless execution, many sectors and companies can create cloud modules and put them online so everyone can exploit them. By making this solution extremely straightforward to use or even by automating everything behind the scenes, people can easily capitalize on recent scientific developments.

Keywords: Serverless AI Pipelines, Automated ML Infrastructure, IaC for Machine Learning Workflows, Scalable Serverless Architectures, Cloud-Native AI Deployment, CI/CD for AI Models, Terraform for AI Infrastructure, Event-Driven AI Processing, Serverless Data Engineering, AI Workflow Automation, Dynamic Resource Provisioning for AI, Stateless AI Inference, DevOps for Machine Learning, IaC-Driven Model Deployment