Capacity Planning for AI: Scaling AI Infrastructure
Strategic capacity planning ensures AI systems can scale efficiently within budget.
13 articles
Strategic capacity planning ensures AI systems can scale efficiently within budget.
Effective incident response minimizes AI system impact and enables quick recovery.
Comprehensive monitoring dashboards enable proactive AI operations management.
LLMOps ensures reliable, cost-effective LLM operations at scale.
LLMOps is essential for reliable LLM applications. Start with prompt management and evaluation, then add observability and cost tracking as you scale.
MLOps is essential for sustainable ML in production. Start with experiment tracking and gradually add components as your ML practice matures.
DataOps maturity directly impacts data reliability and team productivity. Invest in these practices to build a robust data operation.
Production search indexes require operational discipline. From index versioning to blue-green deploys, these are the ops patterns I use to avoid downtime…
Right-sizing Fabric capacity is a finance-and-engineering problem: choose a capacity SKU that matches peak loading patterns, instrument CU consumption, and…
Operational resilience is the unsung prerequisite for AI adoption. Practical SRE for AI means instrumenting model performance, bounding cost exposure, and…
One of my first questions when evaluating any data platform for enterprise use is "how do I see what's actually running?" — and in the early Fabric…
Service Health tracks three types of events: Service Issues : Active problems affecting Azure services Planned Maintenance : Upcoming maintenance that might…
Resource Health reports four possible states: Available : Resource is healthy and operating normally Unavailable : Resource is not healthy due to Azure or…