intermediate~4h
Model Selection and Benchmarking
How to select the right LLM for your application: navigating public benchmarks, the build-vs-buy decision, and the proprietary vs. open-weight vs. open-source model framework covering cost, control, data privacy, and customization trade-offs.
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Subtopics
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Exercises
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Projects
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Quiz Qs
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Flashcards
▶📚 Prerequisites(2)
🎓 Learning objectives
- •Critique public LLM benchmarks (MMLU, HellaSwag, HumanEval) for benchmark gaming and leakage
- •Apply the build-vs-buy decision framework to LLM selection
- •Compare proprietary, open-weight, and open-source models across cost, control, privacy, and customization dimensions
- •Design a task-specific internal benchmark that predicts real application quality better than public leaderboards
- •Estimate total cost of ownership for hosted APIs vs. self-hosted open-weight models
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📂 Subtopics
Related concepts
MMLUHellaSwagHumanEvalLMSYS Chatbot ArenaLLaMAMistralbuild-vs-buyfine-tuningTCO analysisdata privacybenchmark contaminationtask-specific evaluation
Next to learn
finetuning-peft →llm-optimization →evaluation-pipeline-design →