Course · 11 chapters
KI in der Produktion
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
Die Übersichtskarte des Production-AI-Lernpfads — was jedes Kapitel vermittelt, wie sie zusammenhängen und wo du starten solltest.
- 2AI Testing & Evals
Erstelle Eval-Suiten, fange Prompt-Regressionen ab und hör auf, nach Bauchgefühl zu shippen. Praktische Evaluierungsmuster für AI Engineers.
- 3LLMOps in der Produktion
Deploye, überwache und betreibe KI-Systeme, die im großen Maßstab zuverlässig bleiben.
- 4AI Observability & Agent Tracing
Instrumentiere, debugge und optimiere mehrstufige KI-Agenten in der Produktion.
- 5KI-Sicherheit & Guardrails
Schütze KI-Anwendungen von Prompt Injection bis Compliance — die Sicherheit, die jeder AI Engineer braucht.
- 6KI-Kosten & Modellstrategie
Token-Ökonomie, Modell-Routing und Budgetsteuerung meistern, um KI nachhaltig zu betreiben.
- 7Streaming & Echtzeit-AI-Patterns
Baue responsive AI-Interfaces mit SSE, Streaming-APIs, partiellem JSON-Parsing und Echtzeit-Tool-Calls.
- 8AI-Systemdesign
Entwickle zuverlässige, skalierbare KI-gestützte Anwendungen für den Produktionsbetrieb.
- 9AI-UX-Patterns
Design-Patterns, die Engineers heute implementieren können, um AI-Interfaces zu bauen, denen Nutzer tatsächlich vertrauen.
- 10Modell-Migration & Multi-Provider-Strategie
Bereite dich auf Modell-Abkündigungen vor, baue Abstraktionsschichten und route mit Zuversicht über mehrere Anbieter.
- 11Lasttests für KI-Systeme
Warum k6 und Locust bei Streaming-LLMs lügen — und welche Metriken, Tools und Gates tatsächlich unter Traffic standhalten.
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.