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AI & Machine Learning Advanced Save 33%

LLM Application Architecture: From Prototype to Production

Queues, caching, streaming, fallbacks and budgets: the architecture behind a reliable AI feature.

11 students

PR Created by Priya Raman

  • Last updated August 2026
  • English
  • 14h 40m of material
  • 110 lessons

What you will learn

8 concrete outcomes

Every bullet below is something you will have built, shipped or be able to explain by the time you finish the last lesson.

  • Choose between synchronous and queued execution with a defensible reason
  • Stream model output through your own backend, including reconnection handling
  • Distinguish transient from hopeless failures and retry only the first
  • Add provider fallback with documented quality expectations
  • Enforce per-user and per-request cost budgets in code
  • Route requests to different models by task difficulty and measure the trade
  • Build observability for a system whose output is never deterministic
  • Roll out prompt and model changes without a risky flag day

Course curriculum

8 modules · 110 lessons · 14h 40m of material

14 lessons running 1h 46m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

14 lessons running 1h 52m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

14 lessons running 1h 56m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

14 lessons running 1h 58m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

14 lessons running 1h 50m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

14 lessons running 1h 48m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

13 lessons running 1h 46m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

13 lessons running 1h 44m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

8 modules · 110 lessons

14h 40m total length

Requirements

Short list, and deliberately so. If you meet these you can start today.

  • Experience building and operating production backend services
  • Comfortable with Python, Docker and a message queue
  • You have shipped at least one model-backed feature

About this course

The prototype worked. Then it met real traffic, and every assumption underneath it turned out to be load-bearing. A provider outage takes your feature down. This course is about the architecture that survives all of that.

You work through the request path in depth. Synchronous against queued execution and how to choose, streaming through your own backend to a browser including the reconnection cases, timeouts that respect a user waiting on the other end, and backpressure when demand exceeds what you can serve. Then the reliability layer: retries that distinguish a transient failure from a hopeless one, circuit breakers, provider fallback with quality expectations documented, and graceful degradation that gives the user something useful when the model is unavailable.

Cost architecture gets a full section, treated as a design constraint rather than a monthly surprise. Per-user and per-request budgets enforced in code, semantic caching and its staleness risks, model routing by task difficulty, prompt caching where the provider supports it, and dashboards that attribute spend to features so a product decision can be made with a number.

The final modules cover multi-tenancy and data isolation, observability built for non-deterministic systems, safe rollout of prompt and model changes, and the load and chaos testing that tells you the limits before your users find them.

Topics covered

Frequently asked questions

Still unsure about something? Write to misteryjj100@gmail.com and a human answers, usually the same working day.

No. The reference implementation uses FastAPI, Redis and Postgres because they are widely understood, but every pattern maps onto other stacks and the mapping is discussed.

A load generator is provided that reproduces the failure modes discussed, so you can observe every problem on your own machine before meeting it for real.

The masterclass is about prompt systems and their quality. This course is about infrastructure, reliability and cost. They overlap only at the edges.

Checkout is handled on our provider's secure payment page. The moment your payment clears we email your personal access link and access code to the address you used at checkout, and the same link appears in your account library. There is nothing to install and nothing to wait for.

Email misteryjj100@gmail.com within 14 days of your purchase, quote your order number, and we refund the full amount to your original payment method. No form to fill in and no questions about how much of the course you watched.

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Your instructor

PR

Priya Raman

Applied AI and machine-learning engineer

  • 236 students taught
  • 13 courses published
  • 4.3 instructor rating
  • Machine learning
  • RAG
  • Fine-tuning
  • LLM agents

Priya builds language-model features for a document-heavy SaaS product, which means she has taken retrieval and fine-tuning from a promising notebook to something on call at three in the morning. She teaches the mathematics only where it changes a decision you are about to make, and spends the rest of the time on data quality, evaluation and the cost of an agent that loops. Her courses run on a laptop and a modest API budget, so nobody has to rent a cluster to follow along. She publishes reproducible notebooks alongside every module.

See every course by Priya Raman
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