RETHINKING AI LIFECYCLE MANAGEMENT IN A SERVERLESS WORLD

Abstract

As Artificial Intelligence becomes more widely used across industries, it's becoming clear that traditional DevOps approaches don’t quite fit the way AI development works. AI projects are inherently experimental and require constant iteration, frequent model updates, and access to specialized infrastructure—demands that typical operational frameworks aren't built to handle smoothly. This mismatch often creates friction in development workflows. Serverless computing offers a promising solution. With its event-driven design, automatic scaling, and pay-per-use pricing, serverless aligns naturally with the unpredictable and bursty nature of AI workloads. It can scale up during model training or inference, then scale down when not in use—making it both flexible and cost-effective. This paper takes a close look at how serverless architectures can transform the way we manage the AI lifecycle. By abstracting away infrastructure management, serverless allows data scientists to focus on building better models instead of wrestling with operational challenges. It supports smoother transitions between development, testing, and production environments, and includes helpful features like versioning, A/B testing, and progressive model rollout. The event-driven model of serverless platforms also makes it easier to build continuous training pipelines, automated evaluation systems, and real-time inference services that can adapt based on live performance data. In short, rethinking AI workflows around serverless principles can help organizations move faster, use resources more efficiently, and keep the flexibility needed to experiment and innovate in AI.