1 min read
LLM Observability: What Actually Matters
Latency: P50, P95, P99 response times Cost: Per query, per user, per day Quality: Response relevance, accuracy
Production
40 articles
1 min read
Building Production RAG Systems: Best Practices for 2026
Production RAG is iterative. Start simple, measure everything, and improve based on real user feedback.
1 min read
Implementing Guardrails for Production LLM Applications
LLMs can generate harmful content, leak sensitive information, or be manipulated through prompt injection. A layered defense approach protects users and…
1 min read
Building Production-Ready AI Pipelines with Azure Machine Learning
Many organizations struggle with the gap between experimentation and production. Models that work in notebooks often fail in real-world scenarios due to…
1 min read
Continuous Evaluation: Monitoring AI Quality in Production
Continuous evaluation maintains AI quality standards throughout the system lifecycle.
1 min read
LLMOps: Operationalizing Large Language Models
LLMOps ensures reliable, cost-effective LLM operations at scale.
1 min read
Azure AI Foundry Agents: Building Production AI Agents
Azure AI Foundry simplifies the path from agent prototype to production deployment.
1 min read
AI Observability: Monitoring LLM Applications in Production
Comprehensive observability enables continuous improvement of AI applications.
1 min read
Vision-Language Models in Production: Practical Implementation Guide
VLMs unlock powerful visual understanding capabilities. Deploy them thoughtfully with proper optimization and error handling.
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
MLOps Maturity: Productionizing Machine Learning
MLOps is essential for sustainable ML in production. Start with experiment tracking and gradually add components as your ML practice matures.
1 min read
Production AI Challenges: What They Don't Tell You in Tutorials
Production AI is hard. These challenges require dedicated engineering effort, not just model selection. Plan for them from the start.
1 min read
Custom Model Deployment on Azure: From Fine-Tuning to Production
Best for: Variable workloads, quick start, minimal ops overhead Best for: Predictable workloads, SLA requirements, cost optimization
1 min read
Arize AI for Production LLM Monitoring
Arize provides comprehensive production monitoring for LLMs with enterprise features like drift detection, alerting, and detailed analytics. It's ideal for…
1 min read
LLM Observability Tools: Comparing the Landscape
Choose your observability tools based on your team size, budget, privacy requirements, and existing tooling. Start simple and add more sophisticated tools…
1 min read
Agent Observability: Monitoring AI Systems in Production
Observability transforms AI agents from black boxes into understandable systems. Combine metrics, logs, and traces to gain complete visibility into agent…
1 min read
Agent Safety Patterns: Building Trustworthy AI Systems
Safety in AI agents is not optional - it's foundational. Build safety in from the start, and your agents will be both powerful and trustworthy.
1 min read
Quota Management: Controlling AI Resource Consumption
Effective quota management protects both your users and your budget. Implement quotas at multiple levels - per request, per user, and per organization - to…
1 min read
Rate Limit Handling: Maximizing Throughput Within Constraints
Effective rate limit handling is about working with the API, not against it. Use token buckets, queuing, and adaptive limits to maximize throughput while…
1 min read
Timeout Management in AI Applications: Balancing Speed and Completeness
Timeout management in AI applications requires balancing responsiveness with allowing complex operations to complete. Use adaptive timeouts, deadline…
1 min read
Circuit Breakers for AI Systems: Preventing Cascade Failures
Circuit breakers are essential for AI systems that depend on external APIs. They prevent resource exhaustion, protect downstream services, and enable…
1 min read
Graceful Degradation in AI Systems: Maintaining Service Quality
Graceful degradation is about providing the best possible experience given current constraints. Plan for every level of degradation and communicate clearly…
1 min read
Fallback Patterns for AI Applications: Ensuring Continuity
Fallback patterns ensure your AI application remains useful even when primary services fail. Design your fallbacks to maintain the best possible user…
1 min read
Retry Strategies for AI Applications: Beyond Simple Backoff
Smart retry strategies balance reliability with resource efficiency. The key is adapting behavior based on error type, system state, and business requirements.
1 min read
Error Handling in LLM Applications: A Comprehensive Guide
Robust error handling is what separates prototypes from production systems. Invest in comprehensive error handling early to avoid painful debugging later.
1 min read
Agent Reliability Patterns: Building Robust AI Systems
Building reliable agents requires investment in error handling, recovery mechanisms, and observability. These patterns form the foundation for production AI…
1 min read
AI Agent Best Practices: Lessons from Production
AI Agent Best Practices: Lessons from Production
1 min read
Databricks Vector Search Deep Dive: Production Patterns
Taking Vector Search to production requires careful consideration of performance, reliability, and maintenance. This guide covers production-ready patterns.
1 min read
Monitoring AI Systems in Production
AI systems require specialized monitoring beyond traditional application metrics. This guide covers comprehensive observability for production AI.
1 min read
OpenAI Assistants API: Production Patterns and Best Practices
I started building with the Assistants API in late 2023. In production it rewarded strict state management, clear tool contracts, and thoughtful thread…
1 min read
GPT-4 Turbo in Production: Lessons from Real-World Deployments
GPT 4 Turbo brings several improvements that matter for production: Feature GPT 4 GPT 4 Turbo Context Window 8K/32K 128K Input Cost $0.03/1K $0.01/1K Output…
1 min read
Microsoft Fabric Best Practices for Production
Shipping Fabric into production exposes common anti-patterns — unbounded capacity use, duplicated data copies, and insufficient observability. The best…
1 min read
Tracing and Debugging LLM Applications
Advanced techniques for tracing and debugging complex LLM applications in production.
1 min read
LCEL Design Patterns for Production LLM Applications
Advanced LCEL patterns for building robust, scalable LLM applications in production environments.
1 min read
Circuit Breakers for AI Applications: Preventing Cascade Failures
This concludes our June 2023 series on Azure, Data, and AI topics. We covered: Microsoft Fabric deep dives (Lakehouse, Delta Lake, OneLake) Power BI Direct…
1 min read
Fallback Patterns for AI Applications
Fallback patterns ensure continuous service availability. Tomorrow, I will cover circuit breakers for AI applications.
1 min read
Retry Strategies for AI Applications
Azure OpenAI rate limits in mid-2023 are set per deployment per region, and hitting them is a normal operating condition rather than an exceptional one for…
1 min read
Error Handling for AI Applications: Building Resilient Systems
Robust error handling ensures AI applications remain useful even during failures. Tomorrow, I will cover retry strategies in more depth.
1 min read
Model Monitoring in Production with Azure ML
Comprehensive monitoring ensures your ML models maintain their performance and reliability in production.
2 min read
Repos in Databricks: Managing Production Deployments
Databricks Repos in production use means the code running in your prod workspace is explicitly linked to a specific Git commit—not "whatever notebooks…