Course · 11 chapters
Productie-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
De plattegrond van het Production AI-leerpad — wat elk hoofdstuk je leert, hoe ze op elkaar aansluiten en waar je begint.
- 2AI Testing & Evals
Bouw eval-suites, vang promptregressies op en stop met shippen op gevoel. Praktische evaluatiepatronen voor AI-engineers.
- 3LLMOps in Productie
Deploy, monitor en beheer AI-systemen die betrouwbaar blijven op schaal.
- 4AI-observability en agent-tracing
Instrumenteer, debug en optimaliseer meerstaps AI-agents in productie.
- 5AI-beveiliging & guardrails
Bescherm AI-applicaties van prompt injection tot compliance — de beveiliging die elke AI-engineer nodig heeft.
- 6AI-kosten & modelstrategie
Beheers tokeneconomie, modelroutering en budgetgovernance om AI duurzaam te draaien.
- 7Streaming & Real-Time AI Patterns
Bouw responsieve AI-interfaces met SSE, streaming API's, gedeeltelijke JSON-parsing en real-time tool calls.
- 8AI-systeemontwerp
Ontwerp betrouwbare, schaalbare AI-aangedreven applicaties voor productie.
- 9AI UX-patronen
Designpatronen die engineers vandaag kunnen toepassen om AI-interfaces te bouwen die gebruikers écht vertrouwen.
- 10Modelmigratie en multi-providerstrategie
Bereid je voor op het uitfaseren van modellen, bouw abstractielagen en route met vertrouwen tussen providers.
- 11Load Testing van AI-systemen
Waarom k6 en Locust liegen over streaming LLM's — en de metrics, tools en gates die wél standhouden onder verkeer.
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.