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Elite ML Engineer from Day 1
·
Module 12
LLM Operations: Deployment, Inference, and Monitoring
1
Deploying LLMs with High-Performance Inference Servers
Deploy an LLM for serving using a high-performance inference server like vLLM or TensorRT-LLM.
2
Inference Serving Frameworks: Features & Trade-offs
Compare the features and trade-offs of different inference serving frameworks (e.g., vLLM, TGI, TensorRT-LLM).
3
Model Quantization: Trade-offs and Post-Training Techniques
Explain model quantization trade-offs and implement a post-training quantization technique (e.g., GPTQ, AWQ).
4
Building Robust LLM APIs
Design a robust LLM-powered API endpoint with proper error handling, input validation, and request/response schemas.
5
Optimizing LLM APIs: Caching & Rate-Limiting
Implement caching (semantic and exact) and rate-limiting strategies to manage cost and performance of an LLM API.
6
Accelerating LLM App Engineering with AI Coding Assistants
Integrate AI coding assistants (e.g., Cursor, GitHub Copilot) into the development workflow to accelerate LLM application engineering.
Monitoring LLM Applications
Set up monitoring for a production LLM application to track cost, token usage, latency, and quality metrics.
8
LLM Quality Gates in CI/CD
Design an automated evaluation gate within a CI/CD pipeline to prevent LLM quality regressions.
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