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Fine-Tuning, Distillation, and Model Adaptation
Decide when to fine-tune vs distill, engineer datasets, run SFT / distillation / preference optimization, evaluate, and deploy for cost, latency, and reliability.
12 chaptersFirst chapter free to preview
Full syllabus
2
Evals, Prompts, and the Optimization Flywheel
3
Dataset Design, Curation, and Label Quality
4
Supervised Fine-Tuning from Basics to Production
5
Distillation and Teacher-Student Workflows
6
Preference Optimization and Behavior Shaping
7
Reinforcement-Style Adaptation and Grader Loops
8
Multimodal and Domain-Specific Adaptation
9
Inference, Cost, Latency, and Deployment Tradeoffs
10
Evaluation, Ablation, and Regression Strategy
11
Safety, Drift, Versioning, and Operational Governance
12
Capstones, Production Readiness, and Career Use
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