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LLM Ops Explained: How to Build Scalable AI Systems in 2025
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Most AI demos look cool but fall apart in the real world. That’s where LLM Ops comes in — the engineering discipline that makes large language models reliable, scalable, and production-ready.

In this video, you’ll learn how companies like OpenAI, Anthropic, and Google actually deploy LLM systems at scale — including guardrails, orchestration, evaluation, and monitoring.

If you want to move beyond toy demos and build real AI products, this is your blueprint.

Timestamps
0:00 – Why most AI demos fail in production
1:20 – What is LLM Ops? (MLOps → LLM Ops evolution)
3:05 – Key components of an LLM Ops pipeline
5:45 – Guardrails and safety in LLM systems
8:10 – Evaluating LLMs: Beyond accuracy
10:15 – Scaling LLM applications for millions of users
12:40 – Case study: How FAANG+ teams deploy LLMs
15:05 – Future of LLM Ops (agents, multi-modal, real-time AI)
18:20 – Final takeaways & how to get started
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