How to Build AI Cloud Infrastructure That Stays Up When Everything Else Falls Apart
Downtime has always been expensive. Add AI to the equation and the stakes change in ways that many US enterprises have not fully accounted for. When an AI-powered customer service system goes down, for organizations that have reduced human support capacity in anticipation of AI handling the load, an AI system outage creates a customer service crisis. The consequences of AI infrastructure failures are often more severe than equivalent traditional software failures.
AWS or Google Cloud for AI: The Honest Comparison US Enterprises Need Right Now
Cloud platform decisions are among the most consequential infrastructure choices US enterprises make, and for organizations building serious AI capabilities, the platform choice has implications that extend well beyond storage and compute costs. The AI tooling, model access, MLOps infrastructure, and developer experience differences between AWS and Google Cloud are significant enough to materially affect how quickly and effectively your organization can build and deploy AI systems.
What Is MLOps and Why Every US Enterprise Deploying AI Needs to Understand It
There is a moment that happens in nearly every US enterprise AI project. The model is trained. The accuracy metrics look good. The demo works beautifully. The team deploys to production and moves on. Three months later, the model’s performance has quietly degraded to the point where it is producing worse outcomes than the manual process it replaced, and nobody noticed until the damage was done. This scenario is almost entirely preventable with proper MLOps practice.