GenAI in Production: Scale LLM Apps with FastAPI & LLMOps

About this course

This course contains the use of artificial intelligence.

Stop building GenAI prototypes that fail in production. Learn how to scale, secure, and operate enterprise-grade LLM applications with high reliability, low latency, and full observability.

Who this course is for:

• Senior Software Engineers building scalable GenAI features and microservices.
• Machine Learning & LLMOps Engineers moving models from notebooks to production.
• Backend Developers integrating LLM APIs into enterprise applications.

Cloud & DevOps Engineers designing reliable infrastructure for generative AI.What you will learn:

• Scale GenAI traffic using FastAPI with advanced rate limiting and controls
• Implement automatic multi-provider LLM fallbacks and circuit breakers
• Build production-grade LLMOps evaluation and CI/CD pipelines
• Deploy real-time input and output guardrails to prevent prompt injections
• Optimize latency, time-to-first-token, and infrastructure costs

Implement distributed tracing for LLM applications with OpenTelemetryEnforce token-bucket rate limits and per-tenant usage quotas using RedisExecute zero-downtime prompt version rollouts and canary deployments

Requirements:

• Python proficiency (intermediate level).
• Basic understanding of REST APIs and FastAPI fundamentals.
• Familiarity with Docker and basic cloud deployment concepts.
• Core understanding of LLMs and API-based model providers (OpenAI, Anthropic, or open-source models).

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