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.
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10 articles found
Tag: #Model Deployment
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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.
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.
Model Deployment
5 min read
Load Testing LLM Endpoints: Why Traditional Tools Don't Work
Why standard API load-testing assumptions break for LLM inference, and how to design tests that reflect token generation, concurrency, and real serving bottlenecks.
MLOps
5 min read
Building an Internal ML Platform on Kubernetes: What Actually Works
A pragmatic guide to internal ML platforms on Kubernetes, covering the patterns that reduce platform sprawl and the abstractions teams actually use in production.
Model Deployment
5 min read
LLM Token Economics: Tracking and Controlling Inference Spend
How to measure token-level inference spend in production and add practical controls around prompt size, output limits, routing, caching, and tenant budgets.
MLOps
5 min read
CI/CD for ML Models: Testing Beyond Unit Tests
How to build CI/CD for ML systems with data validation, schema checks, shadow evaluations, and deployment gates that go beyond ordinary application unit tests.
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