LLMOps and Production AI Systems
Learn LLMOps for production AI systems: FastAPI serving, streaming, auth, Docker and Kubernetes basics, tracing, caching, and cost control.
What You'll Learn
Understanding Why a Working Prototype Is Not a Production System
It is the first Monday after acme-assist goes live.
Serving acme-assist with FastAPI and Pydantic
A user's request reaches your code over HTTP, and the answer goes back the same way.
Streaming Responses and Setting Timeouts
A user who asks a question does not want to stare at a blank screen for eight seconds.
Securing the API with Keys, Tenants, and Rate Limits
Every request to an LLM-backed API can cost real money, and if the service can trigger an agent, it can also cost real actions.
Building a Container Image That Runs Safely
Your service has to run somewhere other than your laptop, the same way every time.
Running acme-assist on Kubernetes
One container on one machine works until traffic outgrows it or the machine fails.
Skills You'll Master
Curriculum Index12 topics
Understanding Why a Working Prototype Is Not a Production System
It is the first Monday after acme-assist goes live.
Serving acme-assist with FastAPI and Pydantic
A user's request reaches your code over HTTP, and the answer goes back the same way.
Streaming Responses and Setting Timeouts
A user who asks a question does not want to stare at a blank screen for eight seconds.
Securing the API with Keys, Tenants, and Rate Limits
Every request to an LLM-backed API can cost real money, and if the service can trigger an agent, it can also cost real...
Building a Container Image That Runs Safely
Your service has to run somewhere other than your laptop, the same way every time.
Running acme-assist on Kubernetes
One container on one machine works until traffic outgrows it or the machine fails.
Choosing Between Managed Model APIs and Self-Hosting
Every AI service has to decide how it reaches a model: call a provider's hosted API, or run the weights on...
Tracing Requests with OpenTelemetry
Once the service is live, the question stops being "does it work" and becomes "what is it doing, for whom, and at what...
Caching Prompts and Responses to Cut Cost
Every generated token is metered, and the bill compounds with traffic.
Attributing and Estimating Cost
Caching lowers the bill, but you also need to know where it comes from.
Hands-On Lab: Shipping acme-assist
This lab turns the RAG pipeline into a service. Steps 1 to 8 run free on your laptop with the local model.
Reviewing the Quick Reference and Common Mistakes
Quick reference Common mistakes Calling a synchronous library inside an async def endpoint freezes the event loop for...
Career Impact
Roles that use the skills in this module.
AI Engineer
MLOps Engineer
Platform Engineer
Site Reliability Engineer
Next Modules
Related Guides
Practice on the Coding Sheet
Not a software engineer sheet. Every problem comes from real DevOps, SRE, Platform and Cloud interviews, from your first script to a system you build yourself.
Open the Coding SheetFrequently Asked Questions
MLOps focuses on training, versioning, and serving models you build yourself. LLMOps usually starts from a model someone else trained, so the work shifts to prompts, retrieval, latency, token cost, tracing, and safe rollout of the application around the model.
No. A single container on a managed platform is often enough for a first release. Kubernetes becomes worth its cost when you need several replicas, autoscaling, and consistent rollouts across a team.
Total generation time stays the same. Streaming sends tokens as they are produced, so the user sees the first words in a second or two instead of waiting for the whole answer. The wait feels shorter because progress is visible.
Not by default. A threshold that is too loose returns a cached answer for a question that only looks similar, such as the same policy for a different city. Measure the wrong-hit rate on your own questions, and keep caches separate per tenant.