LLMOps & Deployment

Course Overview

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Free Course

For engineers preparing for LLMOps, ML platform or AI infrastructure interviews who need to talk confidently about running LLMs in production. You will be able to answer questions on serving and GPU sizing, latency and throughput, cost, scaling, reliability, observability, rollouts and model optimization, with worked numbers and real production examples.

Instructor: MantraMindAI

Sections: 6

Course Content

Section 1: Fundamentals & Lifecycle

Prepares you to explain what LLMOps is, how an LLM feature moves from idea to production, and how to control its cost, safety and change management.

Section 2: Serving, APIs & Performance

Prepares you to design the serving path of an LLM app — gateway, rate limits, streaming, structured output, fallbacks, long context and routing — and to reason about its latency and throughput with numbers.

1. What is the gateway pattern for managing LLM APIs?

2. How do you implement rate limiting and request throttling?

3. How do streaming responses improve user experience in real-time systems?

4. What are the key performance metrics for LLM systems (latency, throughput, availability)?

5. How do you ensure reliable structured outputs from LLMs?

6. What fallback strategies can be used when a model fails or is unavailable?

7. How do you handle long context inputs efficiently in production?

8. What is semantic routing, and how does it work in multi-model setups?

Section 3: Monitoring, Logging & Debugging

Prepares you to explain how you would watch an LLM system in production — dashboards, alerts, traces, A/B tests, pipeline failures and drift — and how you would find the cause when quality quietly drops.

1. How do you monitor LLM applications in real time?

2. What logging and tracing strategies help debug LLM pipelines?

3. How do you implement A/B testing for LLM systems?

4. How do you detect failures across multi-step AI pipelines?

5. What tools and metrics are needed for effective observability?

6. How do you monitor data drift in LLM systems over time?

Section 4: Scaling & Reliability

Prepares you to size and autoscale an LLM service for traffic spikes, explain latency spikes, and keep the product working through provider rate limits, outages and lock-in.

1. How do you scale an LLM system to handle high traffic loads?

2. What causes latency spikes, and how can you stabilize performance?

3. How do you handle rate limits from LLM providers?

4. How do you avoid vendor lock-in when relying on one model provider?

5. How do you design systems to handle provider outages gracefully?

6. What is graceful degradation, and how is it applied in AI systems?

Section 5: Optimization & Infrastructure

Prepares you to explain how quantization, pruning, distillation, LoRA and Mixture of Experts make models cheaper to serve or adapt, with the memory and speed numbers behind each choice.

1. What is model quantization, and how does it improve efficiency?

2. What are the trade-offs between static and dynamic quantization?

3. What is pruning, and how does it reduce model size?

4. How does knowledge distillation create smaller, faster models?

5. What is LoRA, and why is it useful for efficient fine-tuning?

6. What is Mixture of Experts (MoE), and how does it optimize compute usage?

Section 6: System Design & Hardware

Prepares you to size GPUs for serving, choose between replication and model parallelism, argue cloud versus on-premise with a break-even calculation, and explain where a vector database fits.

1. What challenges arise when managing GPUs for LLM workloads?

2. How do distributed computing techniques improve LLM scalability?

3. What factors influence the choice between cloud and on-premise deployment?

4. How do vector databases differ from traditional databases in LLM systems?