AI Deployment & LLMOps
The deployment journey: Docker, CI/CD, model serving with vLLM, quantization, KV-cache, speculative decoding, MLOps monitoring, shadow deployments, and incident response. Pulls from the Deployment phase and the Forward Deployed AI Engineering course.
The route
- Deployment & LLMOps (full phase) — ML & AI Engineering (phase 09). 6 lessons: Docker & CI/CD, model serving, optimization & quantization, vLLM serving, MLOps monitoring, and shadow deployments with safe rollout.
- Cloud (AWS) — deployment infrastructure — ML & AI Engineering (phase 10). Cloud infrastructure for AI: S3/IAM, SageMaker, EC2, cost reasoning, and the AWS deployment patterns that show up in every ML infra interview.
- Forward Deployed AI Engineering (full course) — Forward Deployed AI Engineering. Ship production AI on client infrastructure: scope ambiguous problems, integrate with enterprise systems, handle constraints, and deploy in regulated environments.
What you build
An end-to-end deployed AI service with Docker, CI/CD, FastAPI, monitoring, cost tracking, load testing, and an incident response runbook.
Every learning path
Create a free account — the opening phases of 24 of 30 courses are free, no credit card. Or see Pro pricing.
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