In today’s data-driven IT landscape, managing ML lifecycles and operations is converging. On this podcast, we’ll explore how end-to-end ML lifecycle practices extend to proactive, automation-driven IT operations. We'll discuss key MLOps concepts—CI/CD pipelines, feature stores, model monitoring—and how they power anomaly detection, event correlation, and automated remediation.

LLMs can perform complex tasks like drafting contracts or answering medical questions, but without safeguards, they pose serious risks—like leaking PII, giving unauthorized advice, or enabling fraud. NVIDIA’s NeMo Guardrails provides a modular safety framework that enforces AI safety through configurable input and output guardrails, covering risks such as PII exposure, jailbreaks, legal liability, and regulatory violations. In high-stakes areas like healthcare, it blocks unauthorized diagnoses and ensures HIPAA/FDA compliance. Each blocked action includes explainable metadata for auditing and transparency, turning AI safety from a black-box filter into configurable, measurable infrastructure.

Most AI projects don’t fail because of bad models; they fail because of bad data plumbing. Andy Pernsteiner joins the podcast to talk about what it actually takes to build production-grade AI systems that aren’t held together by brittle ETL scripts and data copies. He unpacks why unifying data - rather than moving it - is key to real-time, secure inference, and how event-driven, Kubernetes-native pipelines are reshaping the way developers build AI applications. It’s a conversation about cutting out the complexity, keeping data live, and building systems smart enough to keep up with your models.