Course · 11 chapters
Production AI
Ship and operate AI features in production. The full LLMOps lifecycle across 11 chapters: testing, evals, deployment, observability, guardrails, cost, streaming, and load testing.
What you'll be able to do
- Ship AI features from prototype to production
- Build eval suites that catch regressions
- Debug multi-step AI agents
- Add runtime guardrails to AI systems
- Cut AI costs with a smart model strategy
- Migrate models across providers
What's inside
- 1Production AI: Intro
The map of the Production AI skill path — what each chapter teaches, how they fit together, and where to start
- 2AI Testing & Evals
Build eval suites, catch regressions, and ship AI features with confidence
- 3LLMOps in Production
Deploy, monitor, and operate AI systems that stay reliable at scale
- 4AI Observability & Agent Tracing
Instrument, debug, and optimize multi-step AI agents in production
- 5AI Security & Guardrails
Protect AI applications — from prompt injection defense to EU AI Act compliance
- 6AI Cost & Model Strategy
Master token economics, model routing, and budget governance to run AI sustainably
- 7Streaming & Real-Time AI
SSE, WebSockets, partial JSON parsing, streaming tool calls, and responsive AI interfaces
- 8AI System Design
Architect reliable, scalable AI-native applications for production
- 9AI UX Patterns
Design AI features users actually trust — confidence indicators, graceful failures, and human-in-the-loop
- 10Model Migration & Multi-Provider
Prepare for model deprecations, build abstraction layers, and route across providers with confidence
- 11Load Testing AI Systems
Why k6 and Locust lie about streaming LLMs — TTFT, ITL, goodput, GPU saturation, and SLO-gated load tests that actually predict production
Frequently asked questions
- What does this Production AI course cover?
- It covers the full lifecycle of running AI features in production rather than one narrow topic. Across 11 chapters you work through testing and evals, LLMOps and deployment, observability and agent tracing, guardrails, cost and model strategy, streaming, system design, AI UX patterns, model migration, and load testing.
- How is this different from the deep-dive evals, security, and governance paths?
- This path is the breadth path: it teaches the whole ship-and-operate workflow end to end. The sibling paths go deep on a single area such as LLM evaluation, adversarial security, or AI Act governance. Start here for the operating picture, then drill into a deep dive where you need depth.
- Who is this course for?
- It is built for software and ML engineers who already build with LLMs and now need to ship and run those features reliably. The level is practitioner, so it assumes you can read and write code and have called an LLM API before.
- How long does it take and is there a certificate?
- The path runs about 228 minutes, roughly 3.8 hours, across 11 chapters that you complete at your own pace. Finishing it earns an AI Academy certificate of completion.
- Is this course free?
- No. Production AI is a paid path included with an AI Academy by Anthropos subscription.
Earn a certificate
Complete all chapters to receive your certificate of completion.