Production AI

LLMOps course, ship AI features to production, production AI engineering, AI feature lifecycle, operate LLMs in production, AI observability and agent tracing, LLM cost and model strategy, streaming AI responses, load testing AI systems, multi-provider model migration

practitioner

en

• Take an AI feature from prototype to production across the full LLMOps lifecycle
• Stand up testing and eval suites that catch regressions before release
• Add observability and trace multi-step AI agents to debug real failures
• Operate AI safely with runtime guardrails and a cost and model strategy
• Stream real-time responses and design AI systems and UX that hold up under load
• Migrate models and run multi-provider setups without breaking production
• Load-test AI systems against latency and throughput SLOs

• Url: https://aiacademy.anthropos.work/chapters/production-ai-intro/ · Production AI: Intro · Position: 1 · The map of the Production AI skill path — what each chapter teaches, how they fit together, and where to start
• Url: https://aiacademy.anthropos.work/chapters/ai-testing-evals/ · AI Testing & Evals · Position: 2 · Build eval suites, catch regressions, and ship AI features with confidence
• Url: https://aiacademy.anthropos.work/chapters/llmops-production/ · LLMOps in Production · Position: 3 · Deploy, monitor, and operate AI systems that stay reliable at scale
• Url: https://aiacademy.anthropos.work/chapters/ai-observability/ · AI Observability & Agent Tracing · Position: 4 · Instrument, debug, and optimize multi-step AI agents in production
• Url: https://aiacademy.anthropos.work/chapters/ai-security-guardrails/ · AI Security & Guardrails · Position: 5 · Protect AI applications — from prompt injection defense to EU AI Act compliance
• Url: https://aiacademy.anthropos.work/chapters/ai-cost-model-strategy/ · AI Cost & Model Strategy · Position: 6 · Master token economics, model routing, and budget governance to run AI sustainably
• Url: https://aiacademy.anthropos.work/chapters/streaming-patterns/ · Streaming & Real-Time AI · Position: 7 · SSE, WebSockets, partial JSON parsing, streaming tool calls, and responsive AI interfaces
• Url: https://aiacademy.anthropos.work/chapters/ai-system-design/ · AI System Design · Position: 8 · Architect reliable, scalable AI-native applications for production
• Url: https://aiacademy.anthropos.work/chapters/ai-ux-patterns/ · AI UX Patterns · Position: 9 · Design AI features users actually trust — confidence indicators, graceful failures, and human-in-the-loop
• Url: https://aiacademy.anthropos.work/chapters/model-migration-strategy/ · Model Migration & Multi-Provider · Position: 10 · Prepare for model deprecations, build abstraction layers, and route across providers with confidence
• Url: https://aiacademy.anthropos.work/chapters/load-testing-ai/ · Load Testing AI Systems · Position: 11 · Why k6 and Locust lie about streaming LLMs — TTFT, ITL, goodput, GPU saturation, and SLO-gated load tests that actually predict production

• Take an AI feature from prototype to production across the full LLMOps lifecycle
• Stand up testing and eval suites that catch regressions before release
• Add observability and trace multi-step AI agents to debug real failures
• Operate AI safely with runtime guardrails and a cost and model strategy
• Stream real-time responses and design AI systems and UX that hold up under load
• Migrate models and run multi-provider setups without breaking production
• Load-test AI systems against latency and throughput SLOs

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.

Course Mode: online · Course Workload: PT228M · Mode: online