MLOps and AI infrastructure
Everything between a trained model and a reliable, secure, governed production system, in one map.
Overview
MLOps is the discipline of taking models from experiments to reliable production systems and keeping them healthy. AI infrastructure is the hardware and software underneath: GPUs, serving engines, containers, orchestration, observability, security and governance.
A brilliant recipe is not a restaurant. You also need a kitchen, suppliers, hygiene rules, staff schedules and quality checks. MLOps and AI infrastructure are the restaurant around the model.
From experiment to production
Experiments are tracked in MLflow or Weights & Biases, pipelines run on Kubeflow or managed platforms such as SageMaker and Vertex AI, features are kept consistent by a Feature store, and deployed models are watched by Model monitoring and documented in Model cards.
Serving and hardware
Models are served by Triton, vLLM, SGLang, TensorRT, ONNX Runtime, llama.cpp, KServe, Ray Serve or BentoML, on accelerators such as NVIDIA H100, NVIDIA H200, NVIDIA B200 and Cloud TPU, programmed through CUDA and connected by NCCL. Compression is covered in Compression and serving.
Platform and architecture
Docker, Kubernetes, Helm and ArgoCD run and deploy services; Prometheus, Grafana and OpenTelemetry make them observable. Architecture patterns such as Microservices, Circuit breaker and Saga pattern keep distributed systems reliable.
Security and governance
Access is secured with OAuth 2.0, OIDC, mTLS and Zero Trust; risks are managed with OWASP guidance, privacy law such as GDPR and KVKK, the EU AI Act and an AI governance programme.
The full map
MLOps
Inference and serving
Hardware
Platform
Architecture
Security and governance
Related topics
Frequently asked questions
What is MLOps?
The practices and tools for deploying, monitoring and maintaining machine learning models reliably in production.
Which serving engine should I use for LLMs?
vLLM and SGLang are strong open-source defaults for throughput; TensorRT-LLM and Triton suit NVIDIA-optimised deployments; llama.cpp suits local and CPU use.
Summary
MLOps and AI infrastructure turn models into dependable services: tracked experiments, efficient serving on the right hardware, containerised platforms, observability, resilient architecture, and security and governance by design.