Your years of managing servers, automating deployments, and keeping systems running 24/7 give you an exceptional foundation for MLOps engineering. The transition from traditional system administration to ML infrastructure is one of the most natural paths in the AI engineering landscape. You already understand the operational mindset that many ML practitioners lack. You know what it means to be on-call, to think about failure modes, and to build systems that don't break at 3 AM. Now you're applying those same principles to machine learning workloads. The skills transfer is remarkably direct: Linux administration becomes GPU cluster management, shell scripting evolves into ML pipeline automation, and your monitoring expertise applies to tracking model performance and data drift. Your experience with containerization, networking, and storage systems gives you a head start with Kubernetes-based ML platforms, distributed training, and the massive data pipelines that power modern AI systems. Where you'll need to grow is in understanding the ML-specific aspects. Model versioning differs from code versioning, inference serving has unique latency requirements, and GPU infrastructure introduces new considerations around memory management and parallel processing. But these are extensions of concepts you already know, not entirely new domains. This path takes 5-8 months because you're building on a solid foundation rather than starting from scratch. Timeline: 5-8 months.
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build