AI Reliability
6 min read
Production RAG Systems: A Reliability Checklist
Most RAG systems work in demos and fail in production. Use this checklist to harden retrieval, chunking, evaluation, freshness, and guardrails before users feel the gaps.
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AI Infrastructure Insights & Production Lessons
We share everything we learn — real use cases, real production lessons. Technical deep-dives on MLOps, model deployment, AI reliability, and more.
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Posts authored by the Resilio Tech Team. More in-depth tutorials and case studies coming soon.
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28 articles found
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Model Deployment
8 min read
Serving Open-Source LLMs with vLLM on Kubernetes
A practical guide to deploying open-source LLMs with vLLM on Kubernetes — covering GPU sizing, request routing, autoscaling, batching, and safe rollouts.
AI Reliability
6 min read
Why Your ML Models Fail in Production (And How to Fix It)
Most ML models that work in notebooks break in production. Learn the top reasons for production ML failures — model drift, infrastructure gaps, and monitoring blind spots — and how to fix them.
MLOps
5 min read
AI Observability: Metrics and Dashboards That Actually Matter
A practical guide to AI observability for production systems — including latency, drift, token usage, retrieval quality, and the dashboards teams actually use during incidents.
Model Deployment
6 min read
Model Canary Releases: Shadow Traffic, Rollbacks, and Safe Promotion
A practical guide to rolling out ML models safely in production using shadow traffic, canary promotion, quality gates, and fast rollback paths.
MLOps
7 min read
Building an MLOps Pipeline on Kubernetes: A Practical Guide
A hands-on guide to building production MLOps pipelines on Kubernetes — covering CI/CD for models, automated retraining, model registry integration, and deployment strategies.
AI Reliability
5 min read
AI Incident Response Runbooks for Production Models
How to build practical incident response runbooks for production AI systems, including triage flows for latency spikes, drift, bad outputs, and model-serving failures.
MLOps
5 min read
LLM Gateway Architecture: Routing, Rate Limits, and Cost Controls
How to design an LLM gateway for production use cases, including multi-model routing, guardrails, quotas, usage logging, and cost-aware fallbacks.
Model Deployment
6 min read
GPU Autoscaling: Right-Sizing Inference Clusters Without Over-Provisioning
How to autoscale GPU-backed inference clusters without wasting money, including queue-based scaling, warm capacity, and right-sizing by workload profile.
Model Deployment
7 min read
How We Cut GPU Costs by 40% Without Sacrificing Model Performance
Practical strategies for reducing GPU infrastructure costs — covering spot instances, GPU scheduling, model optimization, and right-sizing — without degrading inference quality.
Model Deployment
5 min read
Multi-Model Serving: Running Dozens of Models on Shared Infrastructure
How to serve many ML models on shared infrastructure without noisy-neighbor problems, unpredictable latency, or runaway GPU spend.
MLOps
5 min read
Batching Strategies for LLM Inference: Throughput vs Latency Tradeoffs
A practical guide to batching LLM inference workloads, including static batching, dynamic batching, queue controls, and when higher throughput starts hurting latency.
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Latest Posts
Production RAG Systems: A Reliability Checklist
3/30/2026 • 6 min read
Serving Open-Source LLMs with vLLM on Kubernetes
3/29/2026 • 8 min read
Why Your ML Models Fail in Production (And How to Fix It)
3/28/2026 • 6 min read
AI Observability: Metrics and Dashboards That Actually Matter
3/27/2026 • 5 min read