Transition from Site Reliability Engineering to AI Engineering by leveraging your deep expertise in system reliability, observability, and infrastructure automation. As an SRE, you already understand the critical principles that make AI systems production-ready: SLO-driven thinking translates directly to AI quality metrics, your monitoring expertise becomes the foundation for ML observability, and your Kubernetes knowledge accelerates model serving deployments. The shift from traditional reliability to AI reliability is more natural than it appears, you're essentially applying your battle-tested operational mindset to a new class of workloads. Your incident response skills become invaluable when debugging model drift, hallucinations, and latency spikes in inference pipelines. This path focuses on understanding ML fundamentals through an operational lens, building robust model serving infrastructure, implementing AI-specific observability, and developing end-to-end MLOps practices. By the end, you'll architect AI systems that are not just functional but production-grade: observable, scalable, and reliable. Timeline: 4-6 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