1 min read
LLMOps: Operationalizing Large Language Models in Production
LLMs present distinct operational challenges: non-deterministic outputs, prompt sensitivity, context window management, and the difficulty of defining…
LLMOps
7 articles
1 min read
AI Observability with Azure Monitor and Application Insights
Monitor these essential AI metrics: latency (p50, p95, p99), token consumption per request, error rates by model and endpoint, cache hit rates for…
1 min read
LLMOps: Operationalizing Large Language Models
LLMOps ensures reliable, cost-effective LLM operations at scale.
1 min read
LLMOps Emergence: Operating Large Language Models in Production
LLMOps is essential for reliable LLM applications. Start with prompt management and evaluation, then add observability and cost tracking as you scale.
1 min read
Evaluation Improvements in Azure AI Studio
Without proper evaluation: Models may hallucinate without detection Quality degrades silently over time Compliance violations go unnoticed User experience…
1 min read
Prompt Flow Improvements: Building Production AI Pipelines
Prompt Flow has evolved significantly since its introduction. Today I'm exploring the latest improvements for building production-ready AI pipelines.
1 min read
Introduction to LLMOps: MLOps for Large Language Models
LLMOps brings discipline to LLM application development. Start with these foundations and iterate as your applications mature.