13 verified briefings on Mlops. Each story includes a plain-English summary, why it matters, and the concrete action engineering teams should take.
AI agents often fail outside of controlled demos because they lack a proper support system. An 'agent harness' provides the necessary infrastructure and guardrails to make them reliable and trustworthy for real-world use.
When real-time AI fails in production, the data pipeline is often the real culprit, not the model. This means teams are often looking in the wrong place to fix issues like latency and degrading accuracy.
Nvidia has released NeMo Switchyard, a new tool for model routing. It helps developers automatically send AI prompts to the most cost-effective model, addressing the growing problem of high inference expenses for businesses.
The open-source AI platform Kubeflow has released major updates, including better Spark support, to simplify building complex AI systems on Kubernetes. The project is also nearing official graduation from the Cloud Native Computing Foundation, signaling its maturity.
An Amazon project using AI for simple coding tasks cost $1.8 million, a staggering 860% over budget. The five-month oversight failure is a stark warning about the hidden financial risks of deploying AI without strict governance.
A new guide argues that securing AI models requires more than just a gateway. It proposes a four-layer 'defense-in-depth' strategy to protect systems at every stage, from execution to output integrity.
Aaron Erickson outlines a shift from basic AI testing to building robust, multi-agent systems. He details architectural patterns for production-grade AI, including combining deterministic guardrails with agentic discovery, optimizing agent hierarchies, and implementing rigorous evaluation frameworks to ensure reliability and scalability.
A new guide explains how to secure the entire AI stack, from initial models to production systems. It provides a roadmap for building resilient AI through layered defense, robust MLOps, and integrated governance.
CoreWeave's CTO, Peter Salanki, discussed the challenges of running AI in production. He highlighted the growing importance of observability, resource utilization, and scheduling for efficient operations. Salanki also advised teams to avoid the common mistake of over-architecting their systems too early.
A new opinion piece warns that the rush to build AI agents is repeating the mistakes of early software development, where deploying apps was as simple and risky as copying a .exe file.
Traditional FinOps practices often recommend downsizing resources with low utilization. However, for certain AI workloads like secure machine learning, low GPU compute usage can be misleading. These tasks may be memory-bound, not compute-bound, making "underutilized" GPUs essential for performance and avoiding higher costs.
Mallika Rao, with experience from Twitter and Netflix, presents a new framework for evaluating production AI systems. She argues traditional metrics are outdated and introduces a five-layer evaluation stack and a maturity model to prevent silent failures and manage "evaluation debt."
AI agent frameworks like CrewAI and AutoGen are moving from demos to production environments for tasks like incident response. This shift is creating a critical new challenge: a lack of established tools and practices for monitoring and observing these complex, multi-step AI systems in real-world applications.