LLMOps & Production

Overview

LLMOps (Large Language Model Operations) encompasses the practices, tools, and infrastructure required to deploy, manage, and optimize LLMs in production environments. This topic bridges the gap between model development and real-world deployment, covering everything from serving infrastructure to cost management.

Why This Matters for Interviews

Production LLM deployment is one of the fastest-growing areas in ML engineering. Interviewers increasingly expect candidates to understand not just how models work, but how to deploy them efficiently at scale. These skills are essential for ML Platform, MLOps, and AI Infrastructure roles.

Learning Objectives

By completing this module, you will be able to:

• Design production-ready LLM serving architectures using industry-standard tools
• Optimize inference costs through intelligent caching, batching, and model selection
• Minimize latency using techniques like speculative decoding, quantization, and streaming
• Implement comprehensive monitoring and observability for LLM applications
• Scale LLM services horizontally with appropriate load balancing and auto-scaling strategies
• Articulate trade-offs between different serving frameworks and deployment patterns

Module Structure

Modules

Module	Description	Key Topics
Serving Infrastructure	Deploy LLMs with production-grade serving frameworks	vLLM, TGI, Triton, TensorRT-LLM, Serverless
Cost Optimization	Minimize inference costs without sacrificing quality	Token economics, caching, model selection
Latency Optimization	Achieve low-latency inference for real-time applications	TTFT, TPS, streaming, speculative decoding
Caching Strategies	Implement intelligent caching for LLM workloads	Semantic cache, KV-cache, prompt caching
Monitoring	Build comprehensive observability for LLM systems	Metrics, logging, tracing, dashboards
Scaling Patterns	Scale LLM services to handle production traffic	Horizontal scaling, auto-scaling, multi-region

Production LLM Architecture Overview

Prerequisites

Before diving into this module, ensure you have foundational knowledge of:

• LLM Fundamentals: Understanding of transformer architectures and attention mechanisms
• Infrastructure Basics: Familiarity with containers, Kubernetes, and cloud services
• Python Programming: Ability to read and write Python for configuration and scripting
• Basic ML Concepts: Understanding of model inference, batching, and GPU utilization

Key Terminology

Term	Definition
TTFT	Time to First Token - latency until the first token is generated
TPS	Tokens Per Second - throughput of token generation
KV-Cache	Key-Value cache storing attention computations for reuse
Continuous Batching	Dynamic batching that adds/removes requests mid-generation
Speculative Decoding	Using a smaller model to propose tokens verified by larger model
Quantization	Reducing model precision (e.g., FP16 to INT8) for efficiency
PagedAttention	Memory-efficient attention mechanism used in vLLM
Tensor Parallelism	Splitting model weights across multiple GPUs

Interview Focus Areas

High-Impact Topics

Based on industry interview patterns, prioritize these areas:

1. Serving Framework Trade-offs: When to use vLLM vs TGI vs TensorRT-LLM
2. Latency vs Throughput: Understanding and optimizing both metrics
3. Cost Modeling: Calculating and optimizing inference costs
4. Caching Strategies: Implementing semantic and KV-cache effectively
5. Scaling Decisions: Horizontal vs vertical scaling considerations

Getting Started

We recommend following the modules in order, as concepts build upon each other:

1. Start with Serving Infrastructure to understand the deployment landscape
2. Move to Cost Optimization to learn about the economics of LLM inference
3. Study Latency Optimization for performance engineering techniques
4. Explore Caching Strategies for efficiency improvements
5. Learn Monitoring for production observability
6. Complete with Scaling Patterns for handling growth

Summary

Aspect	Key Considerations
Serving	Choose framework based on throughput needs, hardware, and model compatibility
Cost	Optimize through caching, batching, and appropriate model selection
Latency	Balance TTFT and TPS based on application requirements
Caching	Layer semantic, KV, and prompt caching for maximum efficiency
Monitoring	Track token-level metrics, costs, and quality indicators
Scaling	Design for horizontal scaling with stateless serving components

Sources

• vLLM Documentation
• Hugging Face TGI Documentation
• NVIDIA Triton Inference Server
• TensorRT-LLM GitHub
• LLM Inference Best Practices - Anyscale
• Efficient LLM Serving - Stanford MLSys Seminar